long-running-app-harness

You orchestrate multi-round AI agent workflows by applying a planner-driven execution harness that enforces deterministic rounds, protocol handshakes, and agent workspace imports for durable, auditable execution across workers and reviewers.

A planner-contract-evaluator loop is a structured workflow mechanism that coordinates planning, contracting, building, evaluating, and artifact handoff across orchestrator, worker, and reviewer roles to ensure guardrails and accountability.

You manage persistent artifacts and run directories by initializing a harness run directory and a product_spec.json file, which guides artifact management and lifecycle within a reusable skill unit across multiple execution rounds.

Yes, a multi-role execution harness enforces protocol handshakes between agents by coordinating orchestrators, workers, and reviewers, ensuring guardrails and accountability during end-to-end application work.

The best way to structure end-to-end AI workflows is using a reusable skill unit that coordinates a planner to contract to build to evaluator loop, managing staged artifacts and guiding the lifecycle within a persistent run directory.

Yes, multi-round application work requires deterministic rounds and artifact handoff across workers to achieve durable, auditable execution, which the harness enforces through protocol handshakes and agent workspace imports.