Building an agent harness with Claude Code

The text discusses the challenges of using single-agent AI systems for coding tasks and proposes a harness-style workflow to improve predictability in AI-assisted development. This approach involves dividing tasks among specialized agents, each with a narrow role and clear handoffs, to prevent issues like context drift, scope creep, and biased self-review. The harness pattern transforms a vague agent session into a structured workflow with phases such as planning, generating, evaluating, and shipping, ensuring each phase has specific roles and gates to verify outputs. The article provides a detailed example of implementing this workflow using Claude Code, where specialized agents (Dev, QE, Ops) work in a gated sequence with telemetry and learning mechanisms to enhance observability and cumulative learning. The pattern is scalable, as demonstrated by the production-ready harness Reygent, and emphasizes the importance of clear specifications, separated contexts, scoped permissions, and observability in ensuring reliable AI workflows.

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