Building a harness framework that genuinely boosts AI agent performance
Drawing on research and hands-on experience, learn how to build an effective AI agent runtime harness. Core strategies include mining historical session errors into rules, keeping instruction files concise, streamlining tools, and enforcing rules using scripts and code.
Added 2026-10-31 · originally published 2026-10-30
Steps
- 1Mine patterns from past sessions, turning historical failures and manual corrections into explicit rules or guardrails.
- 2Keep instruction files brief (keep the main AGENTS.md around 100 lines as a directory pointing to other rules, avoiding a massive dump of all rules).
- 3Streamline and reduce the number of tools, keeping only the most core and impactful ones to minimize distractions and token consumption.
- 4Enforce rules using executable scripts (such as git hooks, linter rules, static code analysis, or custom scripts) rather than relying solely on text prompts.
- 5Regularly maintain and garden your harness by removing redundant rules that cannot prove their effectiveness and cleaning up stale, outdated documentation.
Ideas for Chinese platforms
When running automation scripts or agents for platforms like Xiaohongshu, official accounts, or Xianyu, you can apply the principles of streamlining instruction files and reducing tools/steps. Keep only high-frequency, effective content to prevent long prompts from causing the LLM to lose focus or consume excessive tokens.
Please help me optimize my AI agent harness. Here are the correction logs or failure cases I recently encountered while using my agent: [Paste your agent's historical correction logs or failure cases here] Please help me analyze them by: 1. Extracting specific rules or guardrails to add to the instruction files. 2. Reviewing the existing long-text instructions, streamlining and refactoring them into a main directory file (around 100 lines), and offloading specific rules. 3. Evaluating whether these rules should be enforced programmatically via [git hooks / custom scripts, etc.] rather than relying solely on text prompts.
Related use cases
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