In the news
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers introduced HarnessOpt-Bench, a benchmark measuring how well large language models optimize AI agent harnesses, the prompts, tools, and orchestration code surrounding models.
- Why it matters
- Matters for engineers building agentic systems who need to evaluate whether LLMs can automatically improve system configurations under budget constraints.
- Watch out
- Results show performance varies substantially across tasks and seed regimes, with no clear winner among frontier models or their native harnesses.
- agent
- agentic
- llm
- prompt
- eval
The patterns behind this
- Automatic Prompt Optimization
- Agentic Context Engineering (Evolving Playbook)
- tau-bench (Tool-Agent-User)
Each one covers how the technique works, when it earns its cost, and where it breaks.
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