Harness Engineering for LLM Agent Trajectory Alignment
A new study from arXiv (2605.21516) examines harness engineering as an inference-time technique for large language model agents. The research reframes harness design through trajectory alignment, separating it into task decomposition and guided execution. It finds that more elaborate harnesses are not uniformly better; increased decomposition or guidance can sometimes improve execution but also reduce final task success. The study quantifies how workflow granularity, retry budgets, and action reweighting shape performance limits, and identifies failure modes including over-decomposition, over-pruning, and hallucinated execution.
Key facts
- arXiv paper 2605.21516 studies harness engineering for LLM agents.
- Harness engineering is an inference-time technique.
- It aims to improve long-term performance via task decomposition and guided execution.
- More elaborate harnesses are not uniformly better.
- Increased decomposition or guidance can sometimes reduce task success.
- The study uses a trajectory alignment perspective.
- It separates harness into task decomposition and guided execution.
- Failure modes include over-decomposition, over-pruning, and hallucinated execution.
Entities
Institutions
- arXiv