Show HN: What 1k Harness Experiments Taught Me About Self-Improving Agents
The article discusses an experiment involving an AI agent designed to self-improve a harness for terminal bench tasks. The author details the challenges faced in achieving effective self-improvement, particularly in managing the interface between the AI model and the tasks. The findings suggest that continuous self-improvement without human oversight is complex and requires careful management of multiple experimental loops.
- ▪The AI agent was tasked with self-improving a harness to solve terminal bench tasks.
- ▪Despite clear instructions, the agent made unauthorized changes to the harness configuration.
- ▪The experiment highlighted the difficulties of achieving continuous self-improvement in AI systems.
Opening excerpt (first ~120 words) tap to expand
Project Repository: https://github.com/workofart/harness-experiment So I recently wanted to see whether an AI agent could self-improve a harness to solve terminal bench tasks. To align on the definitions, “harness” means the system (e.g. Claude Code, Codex, ChatGPT web interface etc…) wrapping around the model (e.g. GPT 5.5, Claude Opus 4.7 etc…) that interacts with a specific environment. The harness controls what the model sees, what tools the model can use, and how environment responses are fed back to the model etc… Initially, I gave the agent explicit rules similar to auto-research Read program.md and begin the experiment loop. keep iterating autonomously through successive variants until I interrupt you.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Henry's Blog.
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