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View all sourcesAgent Harness Engineering
- Harness engineering focuses on the scaffolding around AI models, emphasizing that a well-designed harness can significantly enhance an agent's performance, even if the underlying model is less sophisticated. - The harness includes various components such as prompts, tools, context policies, and feedback loops, which collectively enable the model to operate effectively in real-world scenarios. - Mistakes made by agents should be treated as signals for improvement, leading to permanent changes in the harness to prevent future errors, rather than blaming the model itself. - The article highlights the importance of a filesystem and sandboxes for agents, allowing them to manage data and execute code safely, which is crucial for effective long-term operation. - As models improve, the design of harnesses must evolve to address new challenges and leverage the enhanced capabilities of the models, creating a dynamic relationship between model training and harness design. - The concept of "Harness-as-a-Service" is emerging, shifting the focus from building individual models to utilizing pre-built harness frameworks that streamline the development process. Why it matters: Understanding harness engineering is essential for optimizing AI agents, as it shifts the focus from merely selecting models to effectively designing the surrounding infrastructure that enables those models to perform at their best.
Harness Engineering - Martin Fowler.com
- The article discusses the concept of "harness engineering," which aims to build trust in AI-generated code by creating a structured framework (or "harness") around coding agents, enhancing their reliability and reducing the need for human oversight. - It distinguishes between two types of controls: feedforward (guides that anticipate agent behavior) and feedback (sensors that observe and correct post-action), emphasizing the importance of both in improving coding agent performance. - The author categorizes harnesses into three areas: maintainability, architecture fitness, and behavioral regulation, noting that maintainability harnesses are currently the most developed due to existing tooling. - The article highlights the challenges of implementing effective harnesses in legacy codebases, where technical debt complicates the establishment of reliable controls, compared to greenfield projects that can integrate harnessability from the start. - It suggests that coding agents can assist in creating custom controls and static analysis tools, but emphasizes the need for human developers to guide and iterate on these systems to ensure they align with organizational goals and coding standards. - The ongoing development of harness engineering is framed as a critical engineering practice, requiring continuous evaluation and adaptation to maintain coherence and effectiveness as the system evolves. Why it matters: As organizations increasingly rely on AI-driven coding agents, harness engineering will be essential for ensuring code quality, reducing manual oversight, and fostering trust in automated systems.