n-agentic-harnesses-codex

Designing an agentic harness involves selecting a single- or multi-agent shape, defining subsystem boundaries, and establishing core primitives like a tool registry, permission layer, and state machine. This produces a phased MVP plan with acceptance criteria, failure tests, and observability recommendations for maintainable builds.

Making an agent runtime durable requires implementing session state management and a definitive state machine within your harness architecture. This approach ensures the workflow orchestrator safely resumes work after crashes by replaying evals and verifying context assembly.

Evaluating an agentic harness involves identifying missing core primitives and proposing replayable evals to test operational leverage. This process prioritizes necessary upgrades by severity, specifying verification and observability improvements to ensure production safety.

Yes, a single agentic harness architecture applies to both developer copilots and workflow orchestrators. The design recommends specific harness shapes and core boundaries that satisfy shared requirements like context assembly, durability, and tool registries across embedded AI features.

Setting up an evaluation and observability plan requires specifying verification tests and operator UX recommendations within your agent runtime. This defines how to monitor the permission layer and tool registry, ensuring the AI tool behaves safely and predictably in production scenarios.