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AI Engineer summaries
Every long episode from AI Engineer, read in minutes: the key takeaways, the chapters, and a digest of the whole conversation. Talks, workshops, events, and training for AI Engineers.
From Chaos to Choreography: Multi-Agent Orchestration Patterns That Actually Work — Sandipan Bhaumik
Building multi-agent AI systems requires distributed systems thinking. Learn proven choreography vs. orchestration, immutable state management, and failure recovery patterns to move from chaotic demos to reliable production systems.
Platforms for Humans and Machines: Engineering for the Age of Agents — Juan Herreros Elorza
Build AI agent-ready platforms by making them self-service, API-based, and local-first, with excellent documentation and observability.
Why, and how you need to sandbox AI-Generated Code? — Harshil Agrawal, Cloudflare
Treat AI-generated code as untrusted code and apply capability-based security by default denying all access and only allowing necessary actions, using sandboxing techniques like isolates or containers.
Your Insecure MCP Server Won't Survive Production — Tun Shwe, Lenses
Learn how to secure your MCP server for production by focusing on design principles and robust authentication flows like OAuth 2.1 with DCR and CIMD.
Let LLMs Wander: Engineering RL Environments — Stefano Fiorucci
Leverage reinforcement learning with verifiable rewards to train LLMs in dynamic RL environments, moving beyond static SFT data for superior performance and specialized agents.
Bending a Public MCP Server Without Breaking It — Nimrod Hauser, Baz
Optimize agentic tools by curating, wrapping, adding deterministic guardrails, composing, and treating them as functions to improve reliability and performance.
Agentic Engineering: Working With AI, Not Just Using It — Brendan O'Leary
Shift from using AI tools to collaborating with AI agents by mastering context management and adopting a research-plan-implement workflow for higher leverage.
How METR measures Long Tasks and Experienced Open Source Dev Productivity - Joel Becker, METR
This video discusses how METR measures long tasks and developer productivity in the context of open source development.
Identity for AI Agents - Patrick Riley & Carlos Galan, Auth0
Explore the identity challenges and solutions for AI agents in authorization, focusing on fine-grained access control and user approval processes.
OpenAI + @Temporalio : Building Durable, Production Ready Agents - Cornelia Davis, Temporal
Discover how OpenAI's agents SDK and Temporal enable building robust AI applications with durability and seamless orchestration.
Your MCP Server is Bad (and you should feel bad) - Jeremiah Lowin, Prefect
This talk emphasizes designing MCP servers that prioritize agent needs over human API usability, ultimately leading to better AI interactions.
Spec-Driven Development: Sharpening your AI toolbox — Al Harris, Amazon Kiro
Explore Spec-Driven Development to enhance AI toolbox efficiency and achieve high-quality software production through structured workflows.
DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners
DSPy redefines prompt engineering by enabling modular, declarative programming with LLMs, making AI workflows more robust, transferable, and easier to optimize.
Automating Large Scale Refactors with Parallel Agents - Robert Brennan, AllHands
Discover how parallel agents can automate large-scale refactors and tackle tech debt effectively in software engineering.
Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize
Explore how to build a Prompt Learning Loop to optimize AI agents for better performance through effective prompt engineering.
Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel
Learn how to build durable AI agents using the Workflow DevKit and AI SDK for enhanced resumability and observability.
Claude Agent SDK [Full Workshop] — Thariq Shihipar, Anthropic
The video explains Claude Agent SDK, its architecture, benefits, and use cases, emphasizing context-aware agents, powerful bash scripting, and security best practices.
Building Intelligent Research Agents with Manus - Ivan Leo, Manus AI (now Meta Superintelligence)
Manus AI is an action engine that goes beyond answers to execute tasks and automate workflows, accessible via API, Slack, and integrations for broad application.
Jack Morris: Stuffing Context is not Memory, Updating Weights is
Jack Morris discusses the distinction between stuffing context vs. updating weights in AI models, asserting that real learning occurs when information is embedded in model weights.
Shipping AI That Works: An Evaluation Framework for PMs – Aman Khan, Arize
AI product management requires a robust evaluation framework to ensure AI applications meet performance expectations and manage hallucinations effectively.
How Claude Code Works - Jared Zoneraich, PromptLayer
Claude Code represents a breakthrough in AI coding agents through simplified architecture and effective prompt engineering.
Efficient Reinforcement Learning – Rhythm Garg & Linden Li, Applied Compute
Applied Compute optimizes Reinforcement Learning (RL) for enterprises by breaking synchronous bottlenecks, enabling faster, cheaper, and more reliable training through asynchronous pipeline RL and rigorous system modeling.