Explore advanced techniques for building long-running AI agents, moving beyond simple loops. Learn why self-evaluation fails and adversarial evaluators succeed, how to manage context with structured handoffs instead of just compaction, and how to use negotiated 'sprint contracts' and detailed rubrics to build and test complex, full-stack applications autonomously.
Martin Fowler and Kent Beck, two authors of the Agile Manifesto, reunite for an unscripted conversation about their 30-year friendship and the evolution of software engineering. They delve into their practical, daily use of AI (the "Genie"), reflect on the legacy of Extreme Programming and the Agile Manifesto, and offer timeless advice to the next generation of developers.
AI agents are having a moment, and understanding MCP and ADK is key to building them well. Cedric Clyburn and Anna Gutowska explain how MCP powers tool integration while ADK structures reliable multi‑agent systems 🤖. Learn when to use each to build more capable and predictable AI agents.
Lawrence Jones from Incident.io explains why their team needed AI to debug their complex AI SRE product. He details three powerful patterns: a CLI for agents to manage large evaluation files, serializing debug UIs into downloadable file systems for agent-based tracing, and multi-agent pipelines for fleet-scale failure analysis.
Mike Christensen from Ably critiques the standard HTTP streaming (SSE) approach for AI chat applications, highlighting its fragility and limitations. He introduces the "durable session" pattern, a persistent, shared resource built on pub/sub principles, to create resilient, multi-device AI experiences with live, bidirectional control.
Chris Lovejoy argues that winning in vertical AI is an organizational challenge, not just a technical one. He introduces a framework of three roles for domain experts—Oracle, Evaluator, and Architect—to effectively embed their knowledge into AI products, illustrated with case studies from companies like Granola and Anterior.