Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
System Design
Martin Fowler·5 min read
Harness engineering for coding agent users
TL;DR
Coding agents (AI systems that write code autonomously) lack clear guidance on effective usage patterns. Birgitta Böckeler developed a mental model for harness engineering—techniques to control and direct AI coding agents toward desired outcomes.
✦ Why It Matters
Engineers can now apply structured harness engineering practices to control AI coding agents and improve code generation reliability.
Key Takeaways
- Implement feedforward controls to guide coding agents effectively.
- Use feedback controls to enable self-correction in AI-generated code.
- Prioritize quality checks early in the development lifecycle.
- Explore harness templates to standardize coding practices across teams.
How It Works
Harness engineering utilizes a combination of feedforward and feedback controls to enhance the performance of coding agents. Feedforward controls anticipate potential issues and guide the agent's actions, while feedback controls monitor outcomes and facilitate self-correction.
This dual approach helps ensure that coding agents produce higher quality code with fewer errors, ultimately reducing the need for extensive human oversight.