How to Build Agents That Run for Hours (Without Losing the Plot)#
AI Engineer Europe 2026 digest Program Adjacent pick 8–10 April 2026
Ash Prabaker, Andrew Wilson
- Why: Long-running agents fail differently — coherence drift, self-evaluation traps — and the harness techniques here are the reliability engineering of agentic systems.
- What: A workshop covering: why adversarial evaluator agents beat self-evaluation; why structured handoffs beat context compaction for coherence; decomposing work into testable sprint contracts; grading subjective output with LLM-applicable rubrics; traces as the primary debugging loop; and which harness parts to delete when the next model drops.
- How: Adversarial evaluation, contract-based decomposition, rubric grading, and trace-first debugging, as listed in the abstract.
- Where applicable: General agent-harness engineering; no affiliations given and abstract-level detail only — the workshop itself is where the substance will be.