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.
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.
Merve Noyan from Hugging Face discusses how open-source models have achieved parity with closed-source counterparts, highlighting the Hugging Face ecosystem built to support this shift. She covers tools for model selection, local agent deployment, and the transformative "Hugging Face Skills" that allow agents to automate complex ML engineering tasks like fine-tuning models with a single prompt.
Anton Arhipov from JetBrains introduces Koog, a lightweight, Kotlin-native framework for building tool-using LLM agents. This session covers the rationale for using Kotlin in AI, the architecture of Koog agents, and how its graph-based DSL enables the creation of structured, type-safe, and reproducible agent workflows, moving beyond simple prompt-chaining to sophisticated orchestration.
95% of GenAI pilots fail due to feedback integration issues, not deployment challenges. Alessandro Cappelli argues that Reinforcement Learning (RL) provides the only systematic way to incorporate business metrics and production signals to continuously improve models, especially for complex agent-based systems.