AGNTCon + MCPCon Europe 2026

The AI coding conversation has been almost entirely about greenfield: new features, fresh repos, throwaway scripts. Meanwhile the codebases that quietly run hospitals, factories, and banks are maintained by skeleton crews drowning in tribal knowledge, dead build systems, and 20-year-old decisions. Single-LLM code assistants do not survive contact with them.
Agentic systems can, but only with very different workflow patterns than the ones usually demoed. This session is a field report from modernizing a long-lived industrial codebase: what worked, what catastrophically did not, and the patterns that emerged.
We cover the shift from "LLM completes my code" to "agents reason about my codebase": the two-loop research and execute split, vertical slicing, measurement-first execution, structured unknowns, and rollback discipline. We name the failure modes that wreck naive setups on legacy code, including context overflow, hallucinated APIs, false-positive fixes, and agentic drift, and the practices that prevent them.
It closes with where agents still lose, what OSS tooling is missing, and how the community can treat legacy maintenance as a first-class agentic AI problem.