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Agent Harness Engineering

This article explores the concept of Agent Harness Engineering, emphasizing that the scaffolding (prompts, tools, context policies, hooks, sandboxes, etc.) around an AI model is often more critical to agent performance than the model itself. It details harness components, the 'ratchet' of turning mistakes into permanent rules, and strategies for long-running tasks.

SourceO'Reilly AI & ML RadarAuthor: Addy Osmani
Agent Harness Engineering
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Summary only

Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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InvestorsIntermediate

Key points

  • Agent = Model + Harness; the harness includes prompts, tools, execution logic, etc.
  • A good harness with a mediocre model outperforms a great model with a poor harness.
  • Treat every agent mistake as a permanent signal to improve the harness (ratchet principle).
  • Long-horizon work requires harness design patterns like loops, planning, and verification.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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