Podcasts about langchain

LangChain introduces framework for observing and debugging AI agentsBitcoin's "Digital Gold" Status Challenged in Bloomberg Report, Sparking DebateInsider Selling Reaches $25 Million, Signaling Potential Market FloorInvestors Broaden Crypto Interest Amid Market Dip, Executive SaysOpenClaw Discord Bans Users Discussing Bitcoin or Cryptocurrency

פרק מספר 511 של רברס עם פלטפורמה, שהוקלט ב-18 בינואר 2026. אורי ורן מקליטים בכרכור (הגשומה והקרה) ומארחים את נמרוד וקס - CPO ו-Co-Founder של BigID - שחצה את כביש 6 בגשם זלעפות כדי לדבר על אתגרים טכנולוגיים בעולם המופלא של Data Production ו-Security.

Harrison Chase, cofounder of LangChain and pioneer of AI agent frameworks, discusses the emergence of long-horizon agents that can work autonomously for extended periods. Harrison breaks down the evolution from early scaffolding approaches to today's harness-based architectures, explaining why context engineering - not just better models - has become fundamental to agent development. He shares insights on why coding agents are leading the way, the role of file systems in agent workflows, and how building agents differs from traditional software development - from the importance of traces as the new source of truth to memory systems that enable agents to improve themselves over time. Hosted by Sonya Huang and Pat Grady

Samya est Generative AI Research Lead chez Pigment, la nouvelle licorne française qui a levé +230 millions de dollars.On aborde :

In this episode, we talk with Aaron Jorgensen about how JobHive came to life - starting as a small résumé-parsing experiment and gradually growing into a structured, AI-supported interview workflow. Aaron explains how the system handles voice capture, transcription, prompts, and AI avatars, and why he moved toward a multi-agent approach instead of relying on one model to do everything.We dig into what “fair scoring” actually means, how cross-checking evaluators and confidence levels work, and why it's important to keep the reasoning behind decisions visible to both employers and candidates.From the builder's perspective, Aaron walks through the practical side of developing the platform: shaping an MVP, working with LangChain, choosing AWS tools that reduce overhead, and dealing with the usual setbacks—broken features, unreliable external services, and the moments that test your patience. He also talks about the routines and habits that helped him stay consistent during the harder stretches.If you're interested in hiring workflows, AI tooling, or the reality of turning a rough prototype into a functioning product, this conversation covers it all.To learn more about Aaron's work, check out his websites or reach out to him on socials:JobHive: https://jobhive.aiAaron's Website: https://ajeema.com/LinkedIn: https://www.linkedin.com/in/mraaronjorgensen/Circle: https://pybites.circle.so/u/22287446___Book mentioned in ep: https://pybitesbooks.com/books/P3EFa-WuMMkC___

This week we want to introduce you to a new podcast from Intercom - Fin Meetups: Conversations with AI Leaders.Explore the promise and practice of AI, from the leaders building it. Hear conversations from founders, engineering, product and customer service leaders, as they discuss the key opportunities and challenges of the AI era.Episode 1 is our Fin x LangChain meetup in Amsterdam, where Fergal Reid, Chief AI Officer, Intercom, building Fin and Marco Perrini, Deployed Engineer, LangChain, shared how Fin evolved from hand-coded experiments to a complex, multi-model system powered by LangGraph.Make sure to follow Fin Meetups: Conversations with AI Leaders:Apple Podcasts: https://podcasts.apple.com/us/podcast/fin-meetups-conversations-with-ai-leaders/id1852609029Spotify: https://open.spotify.com/show/2Ae5VqOQ0ftClgEI8ltqOqOr wherever you listen to podcasts.If you'd like to join future Fin meetups, subscribe on Luma → https://luma.com/FINSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Gemini 3 is a few days old and the massive leap in performance and model reasoning has big implications for builders: as models begin to self-heal, builders are literally tearing out the functionality they built just months ago... ripping out the defensive coding and reshipping their agent harnesses entirely. Ravin Kumar (Google DeepMind) joins Hugo to breaks down exactly why the rapid evolution of models like Gemini 3 is changing how we build software. They detail the shift from simple tool calling to building reliable "Agent Harnesses", explore the architectural tradeoffs between deterministic workflows and high-agency systems, the nuance of preventing context rot in massive windows, and why proper evaluation infrastructure is the only way to manage the chaos of autonomous loops. They talk through: - The implications of models that can "self-heal" and fix their own code - The two cultures of agents: LLM workflows with a few tools versus when you should unleash high-agency, autonomous systems. - Inside NotebookLM: moving from prototypes to viral production features like Audio Overviews - Why Needle in a Haystack benchmarks often fail to predict real-world performance - How to build agent harnesses that turn model capabilities into product velocity - The shift from measuring latency to managing time-to-compute for reasoning tasks LINKS From Context Engineering to AI Agent Harnesses: The New Software Discipline, a podcast Hugo did with Lance Martin, LangChain (https://high-signal.delphina.ai/episode/context-engineering-to-ai-agent-harnesses-the-new-software-discipline) Context Rot: How Increasing Input Tokens Impacts LLM Performance (https://research.trychroma.com/context-rot) Effective context engineering for AI agents by Anthropic (https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/CloimQsQuJM) Join the final cohort of our Building AI Applications course starting Jan 12, 2026 (https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav): https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav

פרק מספר 505 של רברס עם פלטפורמה - באמפרס מספר 89, שהוקלט ב-13 בנובמבר 2025, רגע אחרי כנס רברסים 2025 [יש וידאו!]: רן, דותן ואלון (והופעת אורח של שלומי נוח!) באולפן הוירטואלי עם סדרה של קצרצרים מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Claude-ים וה-GPT-ים החדשים מהתקופה האחרונה.

How do you build systems with AI? Not code-generating assistants, but production systems that use LLMs as part of their processing pipeline. When should you chain multiple agent calls together versus just making one LLM request? And how do you debug, test, and deploy these things? The industry is clearly in exploration mode—we're seeing good ideas implemented badly and expensive mistakes made at scale. But Google needs to get this right more than most companies, because AI is both their biggest opportunity and an existential threat to their search-based business model.Christina Lin from Google joins us to discuss Agent Development Kit (ADK), Google's open-source Python framework for building agentic pipelines. We dig into the fundamental question of when agent pipelines make sense versus traditional code, exploring concepts like separation of concerns for agents, tool calling versus MCP servers, Google's grounding feature for citation-backed responses, and agent memory management. Christina explains A2A (Agent-to-Agent), Google's protocol for distributed agent communication that could replace both LangChain and MCP. We also cover practical concerns like debugging agent workflows, evaluation strategies, and how to think about deploying agents to production.If you're trying to figure out when AI belongs in your processing pipeline, how to structure agent systems, or whether frameworks like ADK solve real problems versus creating new complexity, this episode breaks down Google's approach to making agentic systems practical for production use.--Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinGoogle Agent Development Kit Announcement: https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/ADK on GitHub: https://google.github.io/adk-docs/Google Gemini: https://ai.google.dev/gemini-apiGoogle Vertex AI: https://cloud.google.com/vertex-aiGoogle AI Studio: https://aistudio.google.com/Google Grounding with Google Search: https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overviewModel Context Protocol (MCP): https://modelcontextprotocol.io/Anthropic MCP Servers: https://github.com/modelcontextprotocol/serversLangChain: https://www.langchain.com/Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

Ev Randle is a General Partner @ Benchmark, one of the best funds in venture capital. In their latest fund, they have Mercor ($10BN valuation), Sierra ($10BN valuation), Firework ($4BN valuation), Legora ($2Bn valuation) and Langchain ($1.4Bn valuation). To put this in multiples on invested capital, that is a 60x, two 30x and two 20x. Before Benchmark, Ev was a Partner @ Kleiner Perkins and before Kleiner, Ev was an investor at Founders Fund and Bond.  AGENDA: 05:25 Biggest Investing Lessons from Peter Thiel, Mary Meeker and Mamoon Hamid 14:36 OpenAI Will Be a $TRN Company & OpenAI or Anthropic: Who Wins Coding? 22:27 Why We Should Not Focus on Margin But Gross Dollar Per Customer 30:25 Why AI Labs are the Biggest Threat to AI App Companies 44:26 Do Benchmark Fire Founders? If so… Truly the Best Partner? 54:38 People, Product, Market: Rank 1-3 and Why? 57:36 Why the Mega Funds Have Just Replaced Tiger 01:04:08 GC, Lightspeed and a16z Cannot Do 5x on Their Funds…  01:14:09 Single Biggest Threat to Benchmark  

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the $1.7B acquisition of Security AI, LangChain's massive valuation, and the surprise $300M funding” round for Periodic Labs. They The post SED News: AMD's Big OpenAI Deal, Intel's Struggles, and Apple's AI Long Game appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the $1.7B acquisition of Security AI, LangChain's massive valuation, and the surprise $300M funding” round for Periodic Labs. They The post SED News: AMD's Big OpenAI Deal, Intel's Struggles, and Apple's AI Long Game appeared first on Software Engineering Daily.

In this conversation with Malte Ubl, CTO of Vercel (http://x.com/cramforce), we explore how the company is pioneering the infrastructure for AI-powered development through their comprehensive suite of tools including workflows, AI SDK, and the newly announced agent ecosystem. Malte shares insights into Vercel's philosophy of “dogfooding” - never shipping abstractions they haven't battle-tested themselves - which led to extracting their AI SDK from v0 and building production agents that handle everything from anomaly detection to lead qualification.The discussion dives deep into Vercel's new Workflow Development Kit, which brings durable execution patterns to serverless functions, allowing developers to write code that can pause, resume, and wait indefinitely without cost. Malte explains how this enables complex agent orchestration with human-in-the-loop approvals through simple webhook patterns, making it dramatically easier to build reliable AI applications.We explore Vercel's strategic approach to AI agents, including their DevOps agent that automatically investigates production anomalies by querying observability data and analyzing logs - solving the recall-precision problem that plagues traditional alerting systems. Malte candidly discusses where agents excel today (meeting notes, UI changes, lead qualification) versus where they fall short, emphasizing the importance of finding the “sweet spot” by asking employees what they hate most about their jobs.The conversation also covers Vercel's significant investment in Python support, bringing zero-config deployment to Flask and FastAPI applications, and their vision for security in an AI-coded world where developers “cannot be trusted.” Malte shares his perspective on how CTOs must transform their companies for the AI era while staying true to their core competencies, and why maintaining strong IC (individual contributor) career paths is crucial as AI changes the nature of software development.What was launched at Ship AI 2025:AI SDK 6.0 & Agent Architecture* Agent Abstraction Philosophy: AI SDK 6 introduces an agent abstraction where you can “define once, deploy everywhere”. How does this differ from existing agent frameworks like LangChain or AutoGPT? What specific pain points did you observe in production that led to this design?* Human-in-the-Loop at Scale: The tool approval system with needsApproval: true gates actions until human confirmation. How do you envision this working at scale for companies with thousands of agent executions? What's the queue management and escalation strategy?* Type Safety Across Models: AI SDK 6 promises “end-to-end type safety across models and UI”. Given that different LLMs have varying capabilities and output formats, how do you maintain type guarantees when swapping between providers like OpenAI, Anthropic, or Mistral?Workflow Development Kit (WDK)* Durability as Code: The use workflow primitive makes any TypeScript function durable with automatic retries, progress persistence, and observability. What's happening under the hood? Are you using event sourcing, checkpoint/restart, or a different pattern?* Infrastructure Provisioning: Vercel automatically detects when a function is durable and dynamically provisions infrastructure in real-time. What signals are you detecting in the code, and how do you determine the optimal infrastructure configuration (queue sizes, retry policies, timeout values)?Vercel Agent (beta)* Code Review Validation: The Agent reviews code and proposes “validated patches”. What does “validated” mean in this context? Are you running automated tests, static analysis, or something more sophisticated?* AI Investigations: Vercel Agent automatically opens AI investigations when it detects performance or error spikes using real production data. What data sources does it have access to? How does it distinguish between normal variance and actual anomalies?Python Support (For the first time, Vercel now supports Python backends natively.)Marketplace & Agent Ecosystem* Agent Network Effects: The Marketplace now offers agents like CodeRabbit, Corridor, Sourcery, and integrations with Autonoma, Braintrust, Browser Use. How do you ensure these third-party agents can't access sensitive customer data? What's the security model?“An Agent on Every Desk” Program* Vercel launched a new program to help companies identify high-value use cases and build their first production AI agents. It provides consultations, reference templates, and hands-on support to go from idea to deployed agentFull Video EpisodeTimestamps00:00 Introduction and Malte's Background at Google01:16 Vercel's AI Engineering Philosophy and Ship AI Recap03:19 Deep Dive: Workflows vs Agents Architecture09:33 AI SDK Success Story: Staying Low-Level and Humble16:35 Framework Design Principles and Open Source Strategy19:20 Vercel Agent: AI-Powered DevOps and Anomaly Detection27:06 Internal Agent Use Cases: Lead Qualification and Abuse Analysis29:49 Agent on Every Desk Program and Enterprise Adoption32:13 Python Support and Multi-Language Infrastructure39:42 The Future of AI-Native Security and Development Get full access to Latent.Space at www.latent.space/subscribe

In this conversation with Malte Ubl, CTO of Vercel (http://x.com/cramforce), we explore how the company is pioneering the infrastructure for AI-powered development through their comprehensive suite of tools including workflows, AI SDK, and the newly announced agent ecosystem. Malte shares insights into Vercel's philosophy of "dogfooding" - never shipping abstractions they haven't battle-tested themselves - which led to extracting their AI SDK from v0 and building production agents that handle everything from anomaly detection to lead qualification. The discussion dives deep into Vercel's new Workflow Development Kit, which brings durable execution patterns to serverless functions, allowing developers to write code that can pause, resume, and wait indefinitely without cost. Malte explains how this enables complex agent orchestration with human-in-the-loop approvals through simple webhook patterns, making it dramatically easier to build reliable AI applications. We explore Vercel's strategic approach to AI agents, including their DevOps agent that automatically investigates production anomalies by querying observability data and analyzing logs - solving the recall-precision problem that plagues traditional alerting systems. Malte candidly discusses where agents excel today (meeting notes, UI changes, lead qualification) versus where they fall short, emphasizing the importance of finding the "sweet spot" by asking employees what they hate most about their jobs. The conversation also covers Vercel's significant investment in Python support, bringing zero-config deployment to Flask and FastAPI applications, and their vision for security in an AI-coded world where developers "cannot be trusted." Malte shares his perspective on how CTOs must transform their companies for the AI era while staying true to their core competencies, and why maintaining strong IC (individual contributor) career paths is crucial as AI changes the nature of software development. What was launched at Ship AI 2025: AI SDK 6.0 & Agent Architecture Agent Abstraction Philosophy: AI SDK 6 introduces an agent abstraction where you can "define once, deploy everywhere". How does this differ from existing agent frameworks like LangChain or AutoGPT? What specific pain points did you observe in production that led to this design? Human-in-the-Loop at Scale: The tool approval system with needsApproval: true gates actions until human confirmation. How do you envision this working at scale for companies with thousands of agent executions? What's the queue management and escalation strategy? Type Safety Across Models: AI SDK 6 promises "end-to-end type safety across models and UI". Given that different LLMs have varying capabilities and output formats, how do you maintain type guarantees when swapping between providers like OpenAI, Anthropic, or Mistral? Workflow Development Kit (WDK) Durability as Code: The use workflow primitive makes any TypeScript function durable with automatic retries, progress persistence, and observability. What's happening under the hood? Are you using event sourcing, checkpoint/restart, or a different pattern? Infrastructure Provisioning: Vercel automatically detects when a function is durable and dynamically provisions infrastructure in real-time. What signals are you detecting in the code, and how do you determine the optimal infrastructure configuration (queue sizes, retry policies, timeout values)? Vercel Agent (beta) Code Review Validation: The Agent reviews code and proposes "validated patches". What does "validated" mean in this context? Are you running automated tests, static analysis, or something more sophisticated? AI Investigations: Vercel Agent automatically opens AI investigations when it detects performance or error spikes using real production data. What data sources does it have access to? How does it distinguish between normal variance and actual anomalies? Python Support (For the first time, Vercel now supports Python backends natively.) Marketplace & Agent Ecosystem Agent Network Effects: The Marketplace now offers agents like CodeRabbit, Corridor, Sourcery, and integrations with Autonoma, Braintrust, Browser Use. How do you ensure these third-party agents can't access sensitive customer data? What's the security model? "An Agent on Every Desk" Program Vercel launched a new program to help companies identify high-value use cases and build their first production AI agents. It provides consultations, reference templates, and hands-on support to go from idea to deployed agent

George Werbacher, Head of Security Operations at Live Oak Bank, reviews the practical realities of implementing AI agents in security operations, sharing his journey from exploring tools like Cursor and Claude Code to building custom agents in-house. He also reflects on the challenges of moving from local development to production-ready systems with proper durability and retry logic. The conversation explores how AI is changing the security analyst role from alert analysis to deeper investigation work, why SOAR platforms face significant disruption, and how MCP servers enable natural language interactions across security tools. George offers pragmatic advice on cutting through AI hype, emphasizing that agents augment rather than replace human expertise while dramatically lowering barriers to automation and query language mastery. Through technical insights and leadership perspective, George illuminates how security teams can embrace AI to improve operational efficiency and mean time to detect without inflating budgets, while maintaining the critical human judgment that effective security demands. Topics discussed: Understanding AI's role in augmenting security analysts rather than replacing them, shifting roles toward investigation and threat hunting. Building custom AI agents using Python and exploring frameworks like LangChain to solve specific SecOps use cases. Managing moving agents from local development to production, including retry logic, failbacks, and durability requirements. Implementing MCP servers to enable natural language interactions with security tools, eliminating the need to learn multiple query languages. Navigating AI hype by focusing on solving specific problems and understanding what agents can realistically accomplish. Predicting SOAR platform disruption as agents take over enrichment, orchestration, and response with simpler automation approaches. Removing platform barriers by enabling analysts to use natural language rather than mastering specific tools or query languages. Exploring context management, prompt engineering, and conversation history techniques essential for building effective agentic systems. Adopting tools like Cursor and Claude Code to empower technical security professionals without deep coding backgrounds.  Listen to more episodes:  Apple  Spotify  YouTube Website

Dell Technologies has announced Dell AI Data Platform advancements designed to help enterprises turn distributed, siloed data into faster, more reliable AI outcomes. Why it matters As enterprise AI adoption surges and data grows, organisations need a platform that can securely transform distributed, siloed data into actionable insights. The Dell AI Data Platform, a critical component of the Dell AI Factory, delivers an open, modular foundation to create value from scattered data silos. By decoupling data storage from processing, it eliminates bottlenecks and provides the flexibility needed for AI workloads like training, fine-tuning, retrieval-augmented generation (RAG) or inferencing. The platform, integrated with the NVIDIA AI Data Platform reference design, is powered by four core building blocks: Storage engines for smart data placement and seamless data movement Data engines to turn data into actionable insights Built-in cyber resiliency Data management services Together, they create a scalable, flexible foundation for customers to realise AI's full potential. Dell AI Data Platform storage engines deliver peak AI performance Dell PowerScale and Dell ObjectScale, the Dell AI Data Platform's storage engines, offer the performance, security and multi-protocol access essential for AI data. Dell PowerScale delivers NAS (network-attached storage) simplicity and parallel performance for AI workloads like training, fine-tuning, inferencing and retrieval-augmented generation (RAG) pipelines. With new integration of NVIDIA GB200 and GB300 NVL72 and ongoing software updates, Dell PowerScale delivers reliable performance, simplified management at scale and seamless compatibility with applications and solution stacks. PowerScale F710, which has achieved NVIDIA Cloud Partner (NCP) certification for high-performance storage, delivers 16k+ GPU-scale with up to 5X less rack space, 88% fewer network switches and up to 72% lower power consumption compared to competitors. Dell ObjectScale, the industry's highest-performing object platform, provides extremely performant, scalable S3-native object storage for massive AI workloads. ObjectScale is available as an appliance or through a new software-defined option on Dell PowerEdge servers that is up to 8 times faster than previous-generation all-flash object storage. New advancements improve ObjectScale's speed, scalability and efficiency. S3 over RDMA support will soon enter tech preview. It will offer up to 230% higher throughput, 80% lower latency and 98% lower CPU usage compared to traditional S3. Small object performance and efficiency improvements for large deployments deliver up to 19% higher throughput and up to 18% lower latency for 10KB objects. Deeper AWS S3 integration and bucket-level compression give developers and data scientists better tools to store, move and use large amounts of data. Dell AI Data Platform data engines power real-time AI Dell is also expanding its data engines, the specialised tools in the Dell AI Data Platform that organise, query and activate AI data. Dell's data engines are built in collaboration with trusted AI leaders like NVIDIA, Elastic and Starburst. The new Data Search Engine, developed in collaboration with Elastic, speeds decision-making by allowing customers to interact with data as naturally as asking a question. Designed for tasks like RAG, semantic search and generative AI pipelines, it integrates with MetadataIQ data discovery software to search billions of files on PowerScale and ObjectScale using granular metadata. Developers can build smarter RAG applications in tools like LangChain with the engine, ingesting only updated files to save compute time and keep vector databases current. The Data Analytics Engine, developed in collaboration with Starburst, enables seamless data querying across spreadsheets, databases, cloud warehouses and lakehouses. The new Data Analytics Engine Agentic Layer transforms raw data into business-ready products in...

welcome to wall-e's tech briefing for wednesday, october 22! explore today's key topics in tech: openai atlas launch: openai introduces the atlas web browser, leveraging chatgpt for conversational search, challenging google's dominance in online search and advertising. netflix & generative ai: ceo ted sarandos discusses generative ai as a tool for enhancing, not replacing, creative storytelling, already utilizing it for special effects. langchain's unicorn status: with a $1.25 billion valuation, langchain garners significant investment, cementing its role in aiding ai agent development through its open source framework. call for google regulation: cloudflare's ceo matthew prince advocates for tighter regulations on google's search and ai practices in the u.k., citing competitive concerns. aws outage resolution: amazon resolves a dns issue causing significant internet disruptions, underscoring the dependency on aws infrastructure by major websites and services. stay tuned for more tech insights tomorrow!

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Amey Desai, the Chief Technology Officer at Nexla, speaks with host Sriram Panyam about the Model Context Protocol (MCP) and its role in enabling agentic AI systems. The conversation begins with the fundamental challenge that led to MCP's creation: the proliferation of "spaghetti code" and custom integrations as developers tried to connect LLMs to various data sources and APIs. Before MCP, engineers were writing extensive scaffolding code using frameworks such as LangChain and Haystack, spending more time on integration challenges than solving actual business problems. Desai illustrates this with concrete examples, such as building GitHub analytics to track engineering team performance. Previously, this required custom code for multiple API calls, error handling, and orchestration. With MCP, these operations can be defined as simple tool calls, allowing the LLM to handle sequencing and error management in a structured, reasonable manner. The episode explores emerging patterns in MCP development, including auction bidding patterns for multi-agent coordination and orchestration strategies. Desai shares detailed examples from Nexla's work, including a PDF processing system that intelligently routes documents to appropriate tools based on content type, and a data labeling system that coordinates multiple specialized agents. The conversation also touches on Google's competing A2A (Agent-to-Agent) protocol, which Desai positions as solving horizontal agent coordination versus MCP's vertical tool integration approach. He expresses skepticism about A2A's reliability in production environments, comparing it to peer-to-peer systems where failure rates compound across distributed components. Desai concludes with practical advice for enterprises and engineers, emphasizing the importance of embracing AI experimentation while focusing on governance and security rather than getting paralyzed by concerns about hallucination. He recommends starting with simple, high-value use cases like automated deployment pipelines and gradually building expertise with MCP-based solutions. Brought to you by IEEE Computer Society and IEEE Software magazine.

Alex Merced discusses the rising standards within the Agentic AI Space. Buy Alex Merced’s latest book “Architecting an Apache Iceberg Lakehouse” use discount code mercedconf25 for a 40% discount. https://www.manning.com/books/architecting-an-apache-iceberg-lakehouse Follow Alex on Social at AlexMerced.com

Lance: https://www.linkedin.com/in/lance-martin-64a33b5/How Context Fails: https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.htmlHow New Buzzwords Get Created: https://www.dbreunig.com/2025/07/24/why-the-term-context-engineering-matters.htmlContent Engineering: https://rlancemartin.github.io/2025/06/23/context_engineering/ https://docs.google.com/presentation/d/16aaXLu40GugY-kOpqDU4e-S0hD1FmHcNyF0rRRnb1OU/edit?usp=sharingManus Post: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-ManusCognition Post: https://cognition.ai/blog/dont-build-multi-agentsMulti-Agent Researcher: https://www.anthropic.com/engineering/multi-agent-research-systemHuman-in-the-loop + Memory: https://github.com/langchain-ai/agents-from-scratch- Bitter Lesson in AI Engineering -Hyung Won Chung on the Bitter Lesson in AI Research: Bitter Lesson w/ Claude Code: Learning the Bitter Lesson in AI Engineering: https://rlancemartin.github.io/2025/07/30/bitter_lesson/Open Deep Research: https://github.com/langchain-ai/open_deep_research https://academy.langchain.com/courses/deep-research-with-langgraphScaling and building things that “don't yet work”: - Frameworks -Roast framework at Shopify / standardization of orchestration tools: MCP adoption within Anthropic / standardization of protocols: How to think about frameworks: https://blog.langchain.com/how-to-think-about-agent-frameworks/RAG benchmarking: https://rlancemartin.github.io/2025/04/03/vibe-code/Simon's talk with memory-gone-wrong: https://simonwillison.net/2025/Jun/6/six-months-in-llms/Full Video EpisodeTimestamps00:00 Introduction and Background00:53 The Rise of Context Engineering01:57 Context Engineering vs Prompt Engineering05:56 The Five Categories of Context Engineering10:02 Multi-Agent Systems and Context Isolation14:48 Classical Retrieval vs Agentic Search17:12 LLMs.txt and MCP Servers24:51 Context Pruning and Memory Management37:25 Memory Systems and Human-in-the-Loop42:55 The Bitter Lesson Applied to AI Engineering51:21 Frameworks, Abstractions, and Building for the Future Get full access to Latent.Space at www.latent.space/subscribe

Lance: https://www.linkedin.com/in/lance-martin-64a33b5/ How Context Fails: https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html How New Buzzwords Get Created: https://www.dbreunig.com/2025/07/24/why-the-term-context-engineering-matters.html Content Engineering: https://x.com/RLanceMartin/status/1948441848978309358 https://rlancemartin.github.io/2025/06/23/context_engineering/ https://docs.google.com/presentation/d/16aaXLu40GugY-kOpqDU4e-S0hD1FmHcNyF0rRRnb1OU/edit?usp=sharing Manus Post: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus Cognition Post: https://cognition.ai/blog/dont-build-multi-agents Multi-Agent Researcher: https://www.anthropic.com/engineering/multi-agent-research-system Human-in-the-loop + Memory: https://github.com/langchain-ai/agents-from-scratch - Bitter Lesson in AI Engineering - Hyung Won Chung on the Bitter Lesson in AI Research: https://www.youtube.com/watch?v=orDKvo8h71o Bitter Lesson w/ Claude Code: https://www.youtube.com/watch?v=Lue8K2jqfKk&t=1s Learning the Bitter Lesson in AI Engineering: https://rlancemartin.github.io/2025/07/30/bitter_lesson/ Open Deep Research: https://github.com/langchain-ai/open_deep_research https://academy.langchain.com/courses/deep-research-with-langgraph Scaling and building things that "don't yet work": https://www.youtube.com/watch?v=p8Jx4qvDoSo - Frameworks - Roast framework at Shopify / standardization of orchestration tools: https://www.youtube.com/watch?v=0NHCyq8bBcM MCP adoption within Anthropic / standardization of protocols: https://www.youtube.com/watch?v=xlEQ6Y3WNNI How to think about frameworks: https://blog.langchain.com/how-to-think-about-agent-frameworks/ RAG benchmarking: https://rlancemartin.github.io/2025/04/03/vibe-code/ Simon's talk with memory-gone-wrong: https://simonwillison.net/2025/Jun/6/six-months-in-llms/

Miles Grimshaw is a Partner at Thrive Capital, an investment firm that builds and invests in internet, software, and technology-enabled companies. Thrive recently closed on $5BN in new funds and also announced Thrive Holdings, a permanent capital vehicle to invest in, acquire, and operate businesses for the long term with the strategic application of technology.During his time at Thrive, Miles has led investments in companies like Airtable, Monzo, Benchling, Lattice, and more recently Cursor, a code editor built for programming with AI, which you'll hear us chat about. That team raised a $900 million round at a $9.9B valuation in June.Prior to Thrive, Miles was a General Partner at Benchmark, where he led seed investments, most notably in LangChain.We spoke about trillion dollar companies, silicon valley as an idea, business genetics, practicing scales, and Swedish House Mafia.0:00 - Intro2:14 – “The Era of Doing”6:15 – Startup Capital Intensity in the Age of AI9:14 – The Rise of Trillion Dollar Outcomes15:11 – Silicon Valley as an Idea21:04 – Physics vs Biology-Style Investing25:41 – Business Genetics and Compounding33:04 – Dying of Indigestion and Going Multi-Product35:55 – Co-Pilots, Command Centers, and Defensibility40:07 – Investing Stage Agnostically44:29 – When is VC a Good Capital Instrument?49:18 – Thrive's Core Beliefs53:57 – A Bet vs a Commitment57:49 – The Few Ideas Miles Takes Seriously59:47 – Doing a Few Big Things vs a Million Little Things1:03:54 – Practicing Scales1:06:22 – What Should More People Be Thinking About?

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they discuss the transformative world of Generative AI. Together, they uncover the ways in which generative AI agents are changing the way we interact with technology, automating tasks and delivering new possibilities.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead of Editorial Services.   Nikita: Hi everyone! Last week was Part 2 of our conversation on core AI concepts, where we went over the basics of data science. In Part 3 today, we'll look at generative AI and gen AI agents in detail. To help us with that, we have Himanshu Raj, Principal AI/ML Instructor. Hi Himanshu, what's the difference between traditional AI and generative AI?  01:01 Himanshu: So until now, when we talked about artificial intelligence, we usually meant models that could analyze information and make decisions based on it, like a judge who looks at evidence and gives a verdict. And that's what we call traditional AI that's focused on analysis, classification, and prediction.  But with generative AI, something remarkable happens. Generative AI does not just evaluate. It creates. It's more like a storyteller who uses knowledge from the past to imagine and build something brand new. For example, instead of just detecting if an email is spam, generative AI could write an entirely new email for you.  Another example, traditional AI might predict what a photo contains. Generative AI, on the other hand, creates a brand-new photo based on description. Generative AI refers to artificial intelligence models that can create entirely new content, such as text, images, music, code, or video that resembles human-made work.  Instead of simple analyzing or predicting, generative AI produces something original that resembles what a human might create.   02:16 Lois: How did traditional AI progress to the generative AI we know today?  Himanshu: First, we will look at small supervised learning. So in early days, AI models were trained on small labeled data sets. For example, we could train a model with a few thousand emails labeled spam or not spam. The model would learn simple decision boundaries. If email contains, "congratulations," it might be spam. This was efficient for a straightforward task, but it struggled with anything more complex.  Then, comes the large supervised learning. As the internet exploded, massive data sets became available, so millions of images, billions of text snippets, and models got better because they had much more data and stronger compute power and thanks to advances, like GPUs, and cloud computing, for example, training a model on millions of product reviews to predict customer sentiment, positive or negative, or to classify thousands of images in cars, dogs, planes, etc.  Models became more sophisticated, capturing deeper patterns rather than simple rules. And then, generative AI came into the picture, and we eventually reached a point where instead of just classifying or predicting, models could generate entirely new content.  Generative AI models like ChatGPT or GitHub Copilot are trained on enormous data sets, not to simply answer a yes or no, but to create outputs that look and feel like human made. Instead of judging the spam or sentiment, now the model can write an article, compose a song, or paint a picture, or generate new software code.  03:55 Nikita: Himanshu, what motivated this sort of progression?   Himanshu: Because of the three reasons. First one, data, we had way more of it thanks to the internet, smartphones, and social media. Second is compute. Graphics cards, GPUs, parallel computing, and cloud systems made it cheap and fast to train giant models.  And third, and most important is ambition. Humans always wanted machines not just to judge existing data, but to create new knowledge, art, and ideas.   04:25 Lois: So, what's happening behind the scenes? How is gen AI making these things happen?  Himanshu: Generative AI is about creating entirely new things across different domains. On one side, we have large language models or LLMs.  They are masters of generating text conversations, stories, emails, and even code. And on the other side, we have diffusion models. They are the creative artists of AI, turning text prompts into detailed images, paintings, or even videos.  And these two together are like two different specialists. The LLM acts like a brain that understands and talks, and the diffusion model acts like an artist that paints based on the instructions. And when we connect these spaces together, we create something called multimodal AI, systems that can take in text and produce images, audio, or other media, opening a whole new range of possibilities.  It can not only take the text, but also deal in different media options. So today when we say ChatGPT or Gemini, they can generate images, and it's not just one model doing everything. These are specialized systems working together behind the scenes.  05:38 Lois: You mentioned large language models and how they power text-based gen AI, so let's talk more about them. Himanshu, what is an LLM and how does it work?  Himanshu: So it's a probabilistic model of text, which means, it tries to predict what word is most likely to come next based on what came before.  This ability to predict one word at a time intelligently is what builds full sentences, paragraphs, and even stories.  06:06 Nikita: But what's large about this? Why's it called a large language model?   Himanshu: It simply means the model has lots and lots of parameters. And think of parameters as adjustable dials the model fine tuned during learning.  There is no strict rule, but today, large models can have billions or even trillions of these parameters. And the more the parameters, more complex patterns, the model can understand and can generate a language better, more like human.  06:37 Nikita: Ok… and image-based generative AI is powered by diffusion models, right? How do they work?  Himanshu: Diffusion models start with something that looks like pure random noise.  Imagine static on an old TV screen. No meaningful image at all. From there, the model carefully removes noise step by step to create something more meaningful and think of it like sculpting a statue. You start with a rough block of stone and slowly, carefully you chisel away to reveal a beautiful sculpture hidden inside.  And in each step of this process, the AI is making an educated guess based on everything it has learned from millions of real images. It's trying to predict.   07:24 Stay current by taking the 2025 Oracle Fusion Cloud Applications Delta Certifications. This is your chance to demonstrate your understanding of the latest features and prove your expertise by obtaining a globally recognized certification, all for free! Discover the certification paths, use the resources on MyLearn to prepare, and future-proof your skills. Get started now at mylearn.oracle.com.  07:53 Nikita: Welcome back! Himanshu, for most of us, our experience with generative AI is with text-based tools like ChatGPT. But I'm sure the uses go far beyond that, right? Can you walk us through some of them?  Himanshu: First one is text generation. So we can talk about chatbots, which are now capable of handling nuanced customer queries in banking travel and retail, saving companies hours of support time. Think of a bank chatbot helping a customer understand mortgage options or virtual HR Assistant in a large company, handling leave request. You can have embedding models which powers smart search systems.  Instead of searching by keywords, businesses can now search by meaning. For instance, a legal firm can search cases about contract violations in tech and get semantically relevant results, even if those exact words are not used in the documents.  The third one, for example, code generation, tools like GitHub Copilot help developers write boilerplate or even functional code, accelerating software development, especially in routine or repetitive tasks. Imagine writing a waveform with just a few prompts.  The second application, is image generation. So first obvious use is art. So designers and marketers can generate creative concepts instantly. Say, you need illustrations for a campaign on future cities. Generative AI can produce dozens of stylized visuals in minutes.  For design, interior designers or architects use it to visualize room layouts or design ideas even before a blueprint is finalized. And realistic images, retail companies generate images of people wearing their clothing items without needing real models or photoshoots, and this reduces the cost and increase the personalization.  Third application is multimodal systems, and these are combined systems that take one kind of input or a combination of different inputs and produce different kind of outputs, or can even combine various kinds, be it text image in both input and output.  Text to image It's being used in e-commerce, movie concept art, and educational content creation. For text to video, this is still in early days, but imagine creating a product explainer video just by typing out the script. Marketing teams love this for quick turnarounds. And the last one is text to audio.  Tools like ElevenLabs can convert text into realistic, human like voiceovers useful in training modules, audiobooks, and accessibility apps. So generative AI is no longer just a technical tool. It's becoming a creative copilot across departments, whether it's marketing, design, product support, and even operations.  10:42 Lois: That's great! So, we've established that generative AI is pretty powerful. But what kind of risks does it pose for businesses and society in general?  Himanshu: The first one is deepfakes. Generative AI can create fake but highly realistic media, video, audios or even faces that look and sound authentic.  Imagine a fake video of a political leader announcing a policy, they never approved. This could cause mass confusion or even impact elections. In case of business, deepfakes can be also used in scams where a CEO's voice is faked to approve fraudulent transactions.  Number two, bias, if AI is trained on biased historical data, it can reinforce stereotypes even when unintended. For example, a hiring AI system that favors male candidates over equally qualified women because of historical data was biased.  And this bias can expose companies to discrimination, lawsuits, brand damage and ethical concerns. Number three is hallucinations. So sometimes AI system confidently generate information that is completely wrong without realizing it.   Sometimes you ask a chatbot for a legal case summary, and it gives you a very convincing but entirely made up court ruling. In case of business impact, sectors like health care, finance, or law hallucinations can or could have serious or even dangerous consequences if not caught.  The fourth one is copyright and IP issues, generative AI creates new content, but often, based on material it was trained on. Who owns a new work? A real life example could be where an artist finds their unique style was copied by an AI that was trained on their paintings without permission.  In case of a business impact, companies using AI-generated content for marketing, branding or product designs must watch for legal gray areas around copyright and intellectual properties. So generative AI is not just a technology conversation, it's a responsibility conversation. Businesses must innovate and protect.  Creativity and caution must go together.   12:50 Nikita: Let's move on to generative AI agents. How is a generative AI agent different from just a chatbot or a basic AI tool?  Himanshu: So think of it like a smart assistant, not just answering your questions, but also taking actions on your behalf. So you don't just ask, what's the best flight to Vegas? Instead, you tell the agent, book me a flight to Vegas and a room at the Hilton. And it goes ahead, understands that, finds the options, connects to the booking tools, and gets it done.   So act on your behalf using goals, context, and tools, often with a degree of autonomy. Goals, are user defined outcomes. Example, I want to fly to Vegas and stay at Hilton. Context, this includes preferences history, constraints like economy class only or don't book for Mondays.  Tools could be APIs, databases, or services it can call, such as a travel API or a company calendar. And together, they let the agent reason, plan, and act.   14:02 Nikita: How does a gen AI agent work under the hood?  Himanshu: So usually, they go through four stages. First, one is understands and interprets your request like natural language understanding. Second, figure out what needs to be done, in this case flight booking plus hotel search.  Third, retrieves data or connects to tools APIs if needed, such as Skyscanner, Expedia, or a Calendar. And fourth is takes action. That means confirming the booking and giving you a response like your travel is booked. Keep in mind not all gen AI agents are fully independent.  14:38 Lois: Himanshu, we've seen people use the terms generative AI agents and agentic AI interchangeably. What's the difference between the two?  Himanshu: Agentic AI is a broad umbrella. It refers to any AI system that can perceive, reason, plan, and act toward a goal and may improve and adapt over time.   Most gen AI agents are reactive, not proactive. On the other hand, agentic AI can plan ahead, anticipate problems, and can even adjust strategies.  So gen AI agents are often semi-autonomous. They act in predefined ways or with human approval. Agentic systems can range from low to full autonomy. For example, auto-GPT runs loops without user prompts and autonomous car decides routes and reactions.  Most gen AI agents can only make multiple steps if explicitly designed that way, like a step-by-step logic flows in LangChain. And in case of agentic AI, it can plan across multiple steps with evolving decisions.  On the memory and goal persistence, gen AI agents are typically stateless. That means they forget their goal unless you remind them. In case of agentic AI, these systems remember, adapt, and refine based on goal progression. For example, a warehouse robot optimizing delivery based on changing layouts.  Some generative AI agents are agentic, like auto GPT. They use LLMs to reason, plan, and act, but not all. And likewise not all agentic AIs are generative. For example, an autonomous car, which may use computer vision control systems and planning, but no generative models.  So agentic AI is a design philosophy or system behavior, which could be goal-driven, autonomous, and decision making. They can overlap, but as I said, not all generative AI agents are agentic, and not all agentic AI systems are generative.  16:39 Lois: What makes a generative AI agent actually work?  Himanshu: A gen AI agent isn't just about answering the question. It's about breaking down a user's goal, figuring out how to achieve it, and then executing that plan intelligently. These agents are built from five core components and each playing a critical role.  The first one is goal. So what is this agent trying to achieve? Think of this as the mission or intent. For example, if I tell the agent, help me organized a team meeting for Friday. So the goal in that case would be schedule a meeting.  Number 2, memory. What does it remember? So this is the agent's context awareness. Storing previous chats, preferences, or ongoing tasks. For example, if last week I said I prefer meetings in the afternoon or I have already shared my team's availability, the agent can reuse that. And without the memory, the agent behaves stateless like a typical chatbot that forgets context after every prompt.  Third is tools. What can it access? Agents aren't just smart, they are also connected. They can be given access to tools like calendars, CRMs, web APIs, spreadsheets, and so on.  The fourth one is planner. So how does it break down the goal? And this is where the reasoning happens. The planner breaks big goals into a step-by-step plans, for example checking team availability, drafting meeting invite, and then sending the invite. And then probably, will confirm the booking. Agents don't just guess. They reason and organize actions into a logical path.  And the fifth and final one is executor, who gets it done. And this is where the action takes place. The executor performs what the planner lays out. For example, calling APIs, sending message, booking reservations, and if planner is the architect, executor is the builder.   18:36 Nikita: And where are generative AI agents being used?  Himanshu: Generative AI agents aren't just abstract ideas, they are being used across business functions to eliminate repetitive work, improve consistency, and enable faster decision making. For marketing, a generative AI agent can search websites and social platforms to summarize competitor activity. They can draft content for newsletters or campaign briefs in your brand tone, and they can auto-generate email variations based on audience segment or engagement history.  For finance, a generative AI agent can auto-generate financial summaries and dashboards by pulling from ERP spreadsheets and BI tools. They can also draft variance analysis and budget reports tailored for different departments. They can scan regulations or policy documents to flag potential compliance risks or changes.  For sales, a generative AI agent can auto-draft personalized sales pitches based on customer behavior or past conversations. They can also log CRM entries automatically once submitting summary is generated. They can also generate battlecards or next-step recommendations based on the deal stage.  For human resource, a generative AI agent can pre-screen resumes based on job requirements. They can send interview invites and coordinate calendars. A common theme here is that generative AI agents help you scale your teams without scaling the headcount.   20:02 Nikita: Himanshu, let's talk about the capabilities and benefits of generative AI agents.  Himanshu: So generative AI agents are transforming how entire departments function. For example, in customer service, 24/7 AI agents handle first level queries, freeing humans for complex cases.  They also enhance the decision making. Agents can quickly analyze reports, summarize lengthy documents, or spot trends across data sets. For example, a finance agent reviewing Excel data can highlight cash flow anomalies or forecast trends faster than a team of analysts.  In case of personalization, the agents can deliver unique, tailored experiences without manual effort. For example, in marketing, agents generate personalized product emails based on each user's past behavior. For operational efficiency, they can reduce repetitive, low-value tasks. For example, an HR agent can screen hundreds of resumes, shortlist candidates, and auto-schedule interviews, saving HR team hours each week.  21:06 Lois: Ok. And what are the risks of using generative AI agents?  Himanshu: The first one is job displacement. Let's be honest, automation raises concerns. Roles involving repetitive tasks such as data entry, content sorting are at risk. In case of ethics and accountability, when an AI agent makes a mistake, who is responsible? For example, if an AI makes a biased hiring decision or gives incorrect medical guidance, businesses must ensure accountability and fairness.  For data privacy, agents often access sensitive data, for example employee records or customer history. If mishandled, it could lead to compliance violations. In case of hallucinations, agents may generate confident but incorrect outputs called hallucinations. This can often mislead users, especially in critical domains like health care, finance, or legal.  So generative AI agents aren't just tools, they are a force multiplier. But they need to be deployed thoughtfully with a human lens and strong guardrails. And that's how we ensure the benefits outweigh the risks.  22:10 Lois: Thank you so much, Himanshu, for educating us. We've had such a great time with you! If you want to learn more about the topics discussed today, head over to mylearn.oracle.com and get started on the AI for You course.  Nikita: Join us next week as we chat about AI workflows and tools. Until then, this is Nikita Abraham…  Lois: And Lois Houston signing off!  22:32 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define “agentic,” then get hands-on with how tools like Cursor, Claude Code, and LangChain actually behave inside an established codebase. Our guest, Matt Makai, now VP of Developer Relations at DigitalOcean, creator of Full Stack Python and Plushcap, shares hard-won tactics. We unpack what breaks, from brittle “generate a bunch of tests” requests to agents amplifying technical debt and uneven design patterns. Plus, we also discuss a sane git workflow for AI-sized diffs. You'll hear practical Claude tips, why developers write more bugs when typing less, and where open source agents are headed. Hint: The destination is humans as editors of systems, not just typists of code. Episode sponsors Posit Talk Python Courses Links from the show Matt Makai: linkedin.com Plushcap Developer Content Analytics: plushcap.com DigitalOcean Gradient AI Platform: digitalocean.com DigitalOcean YouTube Channel: youtube.com Why Generative AI Coding Tools and Agents Do Not Work for Me: blog.miguelgrinberg.com AI Changes Everything: lucumr.pocoo.org Claude Code - 47 Pro Tips in 9 Minutes: youtube.com Cursor AI Code Editor: cursor.com JetBrains Junie: jetbrains.com Claude Code by Anthropic: anthropic.com Full Stack Python: fullstackpython.com Watch this episode on YouTube: youtube.com Episode #517 deep-dive: talkpython.fm/517 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

MariaDB is a name with deep roots in the open-source database world, but in 2025 it is showing the energy and ambition of a company on the rise. Taken private in 2022 and backed by K1 Investment Management, MariaDB is doubling down on innovation while positioning itself as a strong alternative to MySQL and Oracle. At a time when many organisations are frustrated with Oracle's pricing and MySQL's cloud-first pivot, MariaDB is finding new opportunities by combining open-source freedom with enterprise-grade reliability. In this conversation, I sit down with Vikas Mathur, Chief Product Officer at MariaDB, to explore how the company is capitalising on these market shifts. Vikas shares the thinking behind MariaDB's renewed focus, explains how the platform delivers similar features to Oracle at up to 80 percent lower total cost of ownership, and details how recent innovations are opening the door to new workloads and use cases. One of the most significant developments is the launch of Vector Search in January 2023. This feature is built directly into InnoDB, eliminating the need for separate vector databases and delivering two to three times the performance of PG Vector. With hardware acceleration on both x86 and IBM Power architectures, and native connectors for leading AI frameworks such as LlamaIndex, LangChain and Spring AI, MariaDB is making it easier for developers to integrate AI capabilities without complex custom work. Vikas explains how MariaDB's pluggable storage engine architecture allows users to match the right engine to the right workload. InnoDB handles balanced transactional workloads, MyRocks is optimised for heavy writes, ColumnStore supports analytical queries, and Moroonga enables text search. With native JSON support and more than forty functions for manipulating semi-structured data, MariaDB can also remove the need for separate document databases. This flexibility underpins the company's vision of one database for infinite possibilities. The discussion also examines how MariaDB manages the balance between its open-source community and enterprise customers. Community adoption provides early feedback on new features and helps drive rapid improvement, while enterprise customers benefit from production support, advanced security, high availability and disaster recovery capabilities such as Galera-based synchronous replication and the MacScale proxy. We look ahead to how MariaDB plans to expand its managed cloud services, including DBaaS and serverless options, and how the company is working on a “RAG in a box” approach to simplify retrieval-augmented generation for DBAs. Vikas also shares his perspective on market trends, from the shift away from embedded AI and traditional machine learning features toward LLM-powered applications, to the growing number of companies moving from NoSQL back to SQL for scalability and long-term maintainability. This is a deep dive into the strategy, technology and market forces shaping MariaDB's next chapter. It will be of interest to database architects, AI engineers, and technology leaders looking for insight into how an open-source veteran is reinventing itself for the AI era while challenging the biggest names in the industry.

Episode 254: What if the future of AI in customer experience is built not by giant platforms but by small, reusable, open source AI agents? Gurdeep Pall, President of AI Strategy at Qualtrics, believes open, modular AI agents will outmaneuver big tech's locked-down systems. In this conversation from the X4 Summit, Gurdeep argues that “experience agents”—task-specific bots that can plug into any stack—will give companies more control, better performance, and real freedom. Closed AI platforms promise convenience, but they trap businesses in rigid walled gardens. Gurdeep argues that modular architectures unlock something better: flexibility, reuse, and evolution. “Break down the agents to very specific functionality,” he says. “And those agents can be invoked by many different agents for different types of tasks.” This isn't just a tech choice. It's a business and philosophical stance. Qualtrics is partnering with LangChain and releasing open connectors to build an ecosystem of interoperable agents. The goal? Let companies mix, match, and scale customer-facing systems without depending on any one vendor. “This is one semantic level up,” he says, comparing today's agentic architectures to the launch of the web and mobile eras. “What agents are going to do for user experience—taking our digital game to the next level—is very exciting.” Guest: Gurdeep Pall, President of AI Strategy, Qualtrics Host: Rob Markey, Partner, Bain & Company Give Us Feedback: https://bit.ly/CCPodcastFeedback Time-Stamped Topics: (00:01) Why Qualtrics is going all-in on open agentic AI (00:04) An overview of the Qualtrics and LangChain partnership (00:06) The modular architecture of “experience agents” (00:08) Why one task might require seven agents (00:09) How specialization allows reuse and scale (00:10) Rejecting the walled garden model (00:11) Making open systems friction-free (00:12) A real-time use case from the X4 stage (00:14) Plug and play simplicity for complex integrations (00:15) Why this is a new digital paradigm Time-Stamped Quotes: [7:00]  “It's about how you break up the task. Like, when you call the human, the human didn't sit there and not do anything and the password got reset. The human went to a piece of software and they went and worked on it. So, what we are talking about here is the combination of software and the human, now organized most efficiently.” [8:00] “ If you're able to break down the agents to very specific functionality, then those agents can be invoked by many different agents for different types of tasks.”  [10:00] “ There is one example of a very small, open system called the Internet, which somehow, through open standards, became one of the most incredible innovations of human beings ever. So what we are trying to do is to take a stand and say, 'We believe in open systems and we want to let our customers know that this is a choice.'”

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Why you should listenSanjeet Mahajan shares his journey building AgentForce agents and custom AI solutions, revealing the critical prompt engineering techniques that eliminate hallucinations and deliver real ROI.Learn the decision framework for when to use AgentForce versus building custom agents with LangChain and CrewAI, plus real case studies from hospitality and real estate showing measurable results.Discover how to create your own "Content Crafter" AI agent that generates marketing ideas in 10 minutes instead of 3-4 weeks, based on your unique business journey and client data.As a Salesforce consultant, you're watching competitors struggle with AgentForce implementation while others race ahead with custom AI agents. The hallucinations, pricing concerns, and lack of clear guidance on when to build versus buy has left many of you spinning your wheels. I see this frustration constantly - talented consultants who know their platforms inside-out but feel lost in the AI maze. In this episode, I talk with Sanjeet Mahajan from Kizzy Consulting, who's spent months in the trenches building both AgentForce and custom agents. We dive deep into the prompt engineering techniques that actually work, the decision framework for choosing platforms, and proven case studies that show real ROI. If you're tired of AI hype and want practical implementation strategies that work, this conversation will give you the roadmap you need.About Sanjeet MahajanSanjeet Mahajan is the Founder & CEO of Kizzy Consulting, a Salesforce Ridge and ISV Partner helping nonprofits, real estate, and homecare teams grow with clean data, smart automation, and human-first design. A seasoned Technical Architect with a deep curiosity for AI, Sanjeet is building intelligent systems that think, act, and adapt—so businesses don't just keep up, they leap ahead.Resources and LinksKizzyconsulting.comSanjeet's LinkedIn profileLangChainCrewAIN8NNotebookLMNapkin.aiPrevious episode: 624 - How to Turn Client Cloud Platform Pain Into Profitable Migration Projects with Jon TopperCheck out more episodes of the Paul Higgins PodcastSubscribe to our YouTube channel: @PaulHigginsMentoringFree Training for AI & Tech Consultants Ready to Stop Trading Time for MoneyJoin our newsletterSuggested resource

Sherwood Callaway, tech lead at 11X, joins us to talk about building digital workers—specifically Alice (an AI sales rep) and Julian (a voice agent)—that are shaking up sales outreach by automating complex, messy tasks.He looks back on his YC days at OpKit, where he first got his hands dirty with voice AI, and compares the wild ride of building voice vs. text agents. We get into the use of Langgraph Cloud, integrating observability tools like Langsmith and Arize, and keeping hallucinations in check with regular Evals.Sherwood and Demetrios wrap up with a look ahead: will today's sprawling AI agent stacks eventually simplify? // BioSherwood Callaway is an emerging leader in the world of AI startups and AI product development. He currently serves as the first engineering manager at 11x, a series B AI startup backed by Benchmark and Andreessen Horowitz, where he oversees technical work on "Alice", an AI sales rep that outperforms top human SDRs.Alice is an advanced agentic AI working in production and at scale. Under Sherwood's leadership, the system grew from initial prototype to handling over 1 million prospect interactions per month across 300+ customers, leveraging partnerships with OpenAI, Anthropic, and LangChain while maintaining consistent performance and reliability. Alice is now generating eight figures in ARR.Sherwood joined 11x in 2024 through the acquisition of his YC-backed startup, Opkit, where he built and commercialized one of the first-ever AI phone calling solutions for a specific industry vertical (healthcare). Prior to Opkit, he was the second infrastructure engineer at Brex, where he designed, built, and scaled the production infrastructure that supported Brex's application and engineering org through hypergrowth. He currently lives in San Francisco, CA.// Related Links~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Sherwood on LinkedIn: /sherwoodcallaway/ #aiengineering Timestamps:[00:00] AI Takes Over Health Calls[05:05] What Can Agents Really Do?[08:25] Who's in Charge—User or Agent?[11:20] Why Graphs Matter in Agents[15:03] How Complex Should Agents Be?[18:33] The Hidden Cost of Model Upgrades[21:57] Inside the LLM Agent Loop[25:08] Turning Agents into APIs[29:06] Scaling Agents Without Meltdowns[30:04] The Monorepo Tangle, Explained[34:01] Building Agents the Open Source Way[38:49] What Production-Ready Agents Look Like[41:23] AI That Fixes Code on Its Own[43:26] Tracking Agent Behavior with OpenTelemetry[46:43] Running Agents Locally with Phoenix[52:55] LangGraph Meets Arise for Agent Control[53:29] Hunting Hallucinations in Agent Traces[56:45] Off-Script Insights Worth Hearing

Scott and Wes break down how to code with and for AI; perfect for skeptics, beginners, and curious devs. They cover everything from Ghost Text and CLI agents to building your own AI-powered apps with embeddings, function calling, and multi-model workflows. Show Notes 00:00 Welcome to Syntax! 03:56 How to interface with AI. 04:07 IDE Ghost Text. 05:45 IDE Chat, Agents. 08:00 CLI Agents. Claude Code. Open Code. Gemini. 11:13 MCP Servers. Context7 14:47 GUI apps. v0. Bolt.new. Lovable. Windsurf. 19:07 Existing Chat app like ChatGPT. 22:37 Building things WITH AI. 23:32 Prompting. 26:53 Streaming VS not streaming. 28:14 Embeddings and Rag. 31:09 MCP Server. CJ's MCP Deep Dive. 32:36 Brought to you by Sentry.io. 33:25 Multi-model, multi-provider. 36:27 npm libs to use to code with AI. OpenAI SDK. AI SDK. Cloudflare Agents. Langchain. Local AI Tensorflow. Transformers.js. Huggingface. 44:12 Processes and exploring. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

AI infrastructure startup LangChain is raising a new round at about $1 billion valuation led by IVP. Learn more about your ad choices. Visit podcastchoices.com/adchoices

You've probably seen the headlines about AI replacing developers, but is AI really coming for our jobs, or is there more going on?In this episode, we break down what's actually happening in the tech industry and how you can use AI to make sure you don't get left behind. We talk about the role of AI in layoffs, how it's changing software development, and why this shift is more evolution than extinction.We cover real-world examples, practical use cases for AI as a developer, and why companies are now hiring for AI skills. Plus, we explain key AI concepts like LLMs, prompts, agents, and tokenisation in classic Hot Girls Code fashion. We also share some of our hot tips on how to use AI as a tool so it can be your bestie instead of your enemy!Whether you're curious, cautious, or fully on board with AI, this episode will help you understand how to work with it—not against it!Where to Find Us:⁠Instragam⁠⁠Tik Tok⁠⁠The Hot Girls Code Website⁠Links mentioned in the episode:Check out our original AI episodes to hear about the basics and ethics behind AI: Episode 16 and Episode 17Learn more about APIs in Episode 68. What is an API?Learn more about some popular pre-trained models: Open AI's GPT, Google's Gemini, and DALL-E Learn more about popular frameworks and libraries: LangChain, LlamaIndex, and TensorFlowLearn more about AI coding assistants: GitHub Copilot, Windsurf (formerly Codeium), and ClineSponsored By: ⁠Trade Me

Pour l'épisode #307 je recevais Maxime Thoonsen. On en débrief avec Julien.🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |Distribué par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Is building the backend for your AI application slowing you down? In this episode of the MongoDB Podcast, host Jesse Hall sits down with Srikar and Jimmy, the creators of Daemo AI, a revolutionary tool designed to eliminate the tedious "plumbing" of backend development.Discover how Daemo AI is building upon deprecated MongoDB features like Realm App Services, creating a more powerful and flexible solution for developers. We dive deep into their tech stack, including Next.js, Deno, and Express , and explore why they chose MongoDB for its speed and flexibility in AI applications. Plus, you'll see a live demo of Daemo's new SDK and CLI , learn how it can generate data migrations and dummy data on the fly , and get a real answer to the big question: Is AI going to take your job? In This Episode, You Will Learn: What Daemo AI is and how it accelerates development. * How to build AI agents and integrate them with frameworks like LangChain. Why MongoDB is the ideal database for rapid-growth startups and AI. The future of developer jobs in the age of AI.

Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Sydney Runkle: linkedin.com LangGraph: github.com LangChain: langchain.com LangGraph Studio: github.com LangGraph (Web): langchain.com LangGraph Tutorials Introduction: langchain-ai.github.io How to Think About Agent Frameworks: blog.langchain.dev Human in the Loop Concept: langchain-ai.github.io GPT-4 Prompting Guide: cookbook.openai.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

In this episode, Ricardo discusses how AI Agents are transforming project management. Unlike traditional tools, these agents are autonomous, understand context, make decisions, and interact with people and systems to deliver value. With the advancement of models like ChatGPT and platforms such as LangChain, Crew AI, and Google NotebookLM, building smart agents has become much easier. They can update schedules, write meeting notes, draft emails, generate reports, and monitor risks—all integrated with tools like Notion, Slack, Trello, and Google Docs. This shift changes the project manager's role to that of an “AI orchestrator.” However, caution is needed due to potential errors, hallucinations, and data security concerns. AI isn't here to replace project managers but to empower them to focus on what truly matters. Listen to the podcast to learn more!

Demetrios, Sam Partee, and Rahul Parundekar unpack the chaos of AI agent tools and the evolving world of MCP (Model Context Protocol). With sharp insights and plenty of laughs, they dig into tool permissions, security quirks, agent memory, and the messy path to making agents actually useful.// BioSam ParteeSam Partee is the CTO and Co-Founder of Arcade AI. Previously a Principal Engineer leading the Applied AI team at Redis, Sam led the effort in creating the ecosystem around Redis as a vector database. He is a contributor to multiple OSS projects including Langchain, DeterminedAI, LlamaIndex and Chapel amongst others. While at Cray/HPE he created the SmartSim AI framework which is now used at national labs around the country to integrate HPC simulations like climate models with AI. Rahul ParundekarRahul Parundekar is the founder of AI Hero. He graduated with a Master's in Computer Science from USC Los Angeles in 2010, and embarked on a career focused on Artificial Intelligence. From 2010-2017, he worked as a Senior Researcher at Toyota ITC working on agent autonomy within vehicles. His journey continued as the Director of Data Science at FigureEight (later acquired by Appen), where he and his team developed an architecture supporting over 36 ML models and managing over a million predictions daily. Since 2021, he has been working on AI Hero, aiming to democratize AI access, while also consulting on LLMOps(Large Language Model Operations), and AI system scalability. Other than his full time role as a founder, he is also passionate about community engagement, and actively organizes MLOps events in SF, and contributes educational content on RAG and LLMOps at learn.mlops.community.// Related LinksWebsites: arcade.devaihero.studio~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Rahul on LinkedIn: /rparundekarConnect with Sam on LinkedIn: /samparteeTimestamps:[00:00] Agents & Tools, Explained (Without Melting Your Brain)[09:51] MVP Servers: Why Everything's on Fire (and How to Fix It)[13:18] Can We Actually Trust the Protocol?[18:13] KYC, But Make It AI (and Less Painful)[25:25] Web Automation Tests: The Bugs Strike Back[28:18] MCP Dev: What Went Wrong (and What Saved Us)[33:53] Social Login: One Button to Rule Them All[39:33] What Even Is an AI-Native Developer?[42:21] Betting Big on Smarter Models (High Risk, High Reward)[51:40] Harrison's Bold New Tactic (With Real-Life Magic Tricks)[55:31] Async Task Handoffs: Herding Cats, But Digitally[1:00:37] Getting AI to Actually Help Your Workflow[1:03:53] The Infamous Varma System Error (And How We Dodge It)

Recorded live at Sequoia's AI Ascent 2025: LangChain CEO Harrison Chase introduces the concept of ambient agents, AI systems that operate continuously in the background responding to events rather than direct human prompts. Learn how these agents differ from traditional chatbots, why human oversight remains essential and how this approach could dramatically scale our ability to leverage AI.

How do you move from dabbling with AI and vibe coding to building real, production-grade software with it? In this episode, Austin Vance, CEO of Focused returns and we transition the conversation from building AI-enabled applications to fostering AI-native engineering teams. Austin shares how generative AI isn't just a shortcut—it's reshaping how we architect, code, and lead. We also get to hear Austin's thoughts on the leaked ‘AI Mandate' memo from Shopify's CEO, Tobi Lutke.  We cover what Austin refers to as ‘AI-driven development', how to win over the skeptics on your teams, and why traditional patterns of software engineering might not be the best fit for LLM-driven workflows.  Whether you're an engineer,product leader, or startup founder, this episode will give you a practical lens on what AI-native software development actually requires—and how to foster adoption on your teams quickly and safely to get the benefits of using AI in product delivery. Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Inside the episode... Why Shopify's leaked AI memo was a "permission slip" for your own team The three personas in AI adoption: advocates, skeptics, and holdouts How AI-driven development (AIDD) differs from “AI-assisted” workflows Tools and practices Focused uses to ship faster and cheaper with AI Pair programming vs. pairing with an LLM: similarities and mindset shifts How teams are learning to prompt effectively—without prompt engineering training Vibe coding vs. integrating with entrenched systems: what's actually feasible Scaling engineering culture around non-determinism and experimentation Practical tips for onboarding dev teams to tools like Cursor, Windsurf, and Vercel AI SDK Using LLMs for deep codebase exploration, not just code generation Mentioned in this episode Cursor Windsurf LangChain Claude GPT-4 / ChatGPT V0.dev GitHub Copilot Focused (focused.io) Shopify internal AI memo Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence

What does it actually take to build agentic AI applications that hold up in the real world? In this episode, Ashok sits down with Austin, founder of Focused, to share field stories and hard-won lessons from building AI systems that go beyond flashy demos. From legal assistants to government transparency tools, Austin breaks down the concrete criteria for identifying where AI makes sense — and where it doesn't. They unpack how to find the right starting point for your first agentic app, why integration with legacy systems is the real hurdle, and the engineering must-haves that keep AI behavior safe and reliable. You'll hear practical guidance on designing eval frameworks, using abstraction layers like LangChain, and how observability can shape your development roadmap just like in traditional software. Whether you're a product leader or a CTO, this conversation will help you distinguish hype from real opportunity in AI. Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Inside the episode... A practical checklist for identifying your first AI-powered app The hidden cost of "AI for AI's sake" and where traditional software is better Why repetitive knowledge work is prime territory for automation How Focused helped Hamlet build an AI for parsing government meeting data Where read-only data access gives you a safe starting point Why integration is often more complex than the AI itself The importance of eval frameworks and test-driven LLM development How to use observability to continuously improve AI agent behavior Speed vs. believability: surprising lessons from Groq-powered inference Using multiple models in one system and LLMs to QA each other Mentioned in this episode Hamlet (government transparency startup) - https://www.myhamlet.com/?convergence  LangChain - https://www.langchain.com/?convergence  Groq - https://groq.com/?convergence  Claude (Anthropic) - https://claud.ai/?convergence  Dspy Prompting framework - https://dspy.ai/?convergence  Shopify AI memo (referenced) - https://convergence.fm/episode/shopifys-leaked-ai-mandate-explained-6-takeaways-for-your-product-team?convergence Amazon Bedrock / SageMaker - https://aws.amazon.com/bedrock/?convergence Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence

**(Note: Spotify listeners can also watch the screen sharing video accompanying the audio. Other podcast platforms offer the audio-only version.)**In this episode of MongoDB Podcast Live, host Shane McAllister is joined by Sachin Hejip from Dataworkz. Sachin will showcase “Dataworkz Agent Builder” which is built with MongoDB Atlas Vector Search, and demonstrate how it can use Natural Language to create Agents and in turn, automate and simplify the creation of Agentic RAG applications. Sachin will demo the MongoDB Leafy Portal Chatbot Agent, which combines operational data with unstructured data for personalised customer experience and support, built using Dataworkz and MongoDB.Struggling with millions of unstructured documents, legacy records, or scattered data formats? Discover how AI, Large Language Models (LLMs), and MongoDB are revolutionizing data management in this episode of the MongoDB Podcast.Join host Shane McAllister and the team as they delve into tackling complex data challenges using cutting-edge technology. Learn how MongoDB Atlas Vector Search enables powerful semantic search and Retrieval Augmented Generation (RAG) applications, transforming chaotic information into valuable insights. Explore integrations with popular frameworks like Langchain and Llama Index.Find out how to efficiently process and make sense of your unstructured data, potentially saving significant costs and unlocking new possibilities.Ready to dive deeper?#MongoDB #AI #LLM #LargeLanguageModels #VectorSearch #AtlasVectorSearch #UnstructuredData #Podcast #DataManagement #Dataworkz #Observability #Developer #BigData #RAG

**(Note: Spotify listeners can also watch the screen sharing video accompanying the audio. Other podcast platforms offer the audio-only version.)**Struggling with millions of unstructured documents, legacy records, or scattered data formats? Discover how AI, Large Language Models (LLMs), and MongoDB are revolutionizing data management in this episode of the MongoDB Podcast.Join host Shane McAllister and the team as they delve into tackling complex data challenges using cutting-edge technology. Learn how MongoDB Atlas Vector Search enables powerful semantic search and Retrieval Augmented Generation (RAG) applications, transforming chaotic information into valuable insights. Explore integrations with popular frameworks like Langchain and Llama Index.Find out how to efficiently process and make sense of your unstructured data, potentially saving significant costs and unlocking new possibilities.Ready to dive deeper?#MongoDB #AI #LLM #LargeLanguageModels #VectorSearch #AtlasVectorSearch #UnstructuredData #Podcast #DataManagement #RAG #SemanticSearch #Langchain #LlamaIndex #Developer #BigData

In this episode: Jason sits down with Harrison Chase, CEO of LangChain, to explore how AI-powered agents are transforming the way startups operate. They discuss the shift from traditional entry-level roles to AI-driven automation, the importance of human-in-the-loop systems, and the future of AI-powered assistants in business. Harrison shares insights on how companies like Replit, Klarna, and GitLab are leveraging AI agents to streamline operations, plus a look ahead at what's next for AI-driven workflows. Brought to you in partnership with Google Cloud.*Timestamps:(0:00) Introduction to Startup Basics series & Importance of AI in startups(2:04) Partnership with Google Cloud & Introducing Harrison Chase from Langchain(4:38) Evolution of entry-level jobs & Examples of AI agents in startups(8:00) Challenges & Future of AI agents in startups(14:24) AI agents in collaborative spaces & Non-developers creating AI agents(18:40) Closing remarks and where to learn more*Uncover more valuable insights from AI leaders in Google Cloud's 'Future of AI: Perspectives for Startups' report. Discover what 23 AI industry leaders think about the future of AI—and how it impacts your business. Read their perspectives here: https://goo.gle/futureofai*Check out all of the Startup Basics episodes here: https://thisweekinstartups.com/basicsCheck out Google Cloud: https://cloud.google.com/Check out LangChain: https://www.langchain.com/*Follow Harrison:LinkedIn: https://www.linkedin.com/in/harrison-chase-961287118/X: https://x.com/hwchase17*Follow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanis*Follow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

LangChain is a popular open-source framework to build applications that integrate LLMs with external data sources like APIs, databases, or custom knowledge bases. It's commonly used for chatbots, question-answering systems, and workflow automation. Its flexibility and extensibility have made it something of a standard for creating sophisticated AI-driven software. Erick Friis is a Founding Engineer The post LangChain and Agentic AI Engineering with Erick Friis appeared first on Software Engineering Daily.

Episode 653: Shaan Puri ( https://x.com/ShaanVP ) talks to Furqan Rydhan ( https://x.com/FurqanR ) about the biggest opportunities in AI right now.  — Show Notes:  (0:00) Intro (4:42) Define the Job-to-be-done (8:20) How to build an AI Agent workflow (11:16) AI Tools break down (27:05) How Polymarket won (31:48) Why VR is a sleeping giant? (44:43) Be a lifelong player in tech (58:52) The unbeatable combination (1:02:27) Adam Foroughi's A+ execution (1:18:35) Betting on -1 to 0 — Links: • Furqan's site - https://furqan.sh/  • Founders, Inc - https://f.inc/  • Applovin - https://www.applovin.com/  • Claude - https://claude.ai/  • OpenAI - https://platform.openai.com/  • Langchain - https://www.langchain.com/  • AutoGen - https://autogenai.com/  • Crew - https://www.crewai.com/  • CloudSDK - https://cloud.google.com/sdk/  • Perplexity - https://www.perplexity.ai/  • “Attention is all you need” - https://typeset.io/papers/attention-is-all-you-need-1hodz0wcqb  • Anthropic - https://www.anthropic.com/  • Third Web - https://thirdweb.com/  • Luna's AI Brain - https://terminal.virtuals.io/  • Oasis - https://oasis.decart.ai/welcome  • Polymarket - https://polymarket.com/  • Gorilla Tag - https://www.gorillatagvr.com/  • Yeeps - https://tinyurl.com/59z2yrdu  — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano