Continually improving our agent harness

In the development of the Cursor agent harness, a vision-driven process is used to create a robust software product by forming hypotheses, running experiments, and iterating through feedback from evaluations and real usage. The harness is optimized to match model strengths and includes dynamic context management, moving away from static context and strict guardrails as models improve. Online and offline tests, including public benchmarks and A/B testing, assess changes to the harness, focusing on metrics like latency and agent-generated code quality. The system is designed to detect and repair errors, with specific classifications for expected and unknown errors, and utilizes automated tools to handle issues efficiently. The harness is customized for different models, ensuring compatibility with model-specific tool formats and prompting methods. Challenges such as mid-chat model switching are addressed through custom instructions and conversation summarization. The harness is envisioned as key to the future of AI-assisted software engineering, where multiple specialized agents collaborate within a coherent workflow.

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