harness-engineering

# Harness Engineering <skill_contract> <input>An agent workflow, runtime environment, tools, data sensitivity, effects, cadence, risk, and operator constraints.</input> <output>An auditable runtime kernel with scoped permissions, scheduling, observability, recovery, and eval controls.</output> <done>An end-to-end trace and failure-path tests prove bounded, replayable, recoverable delegated action.</done> <non_goals>Business-task decomposition, prompt-only safety, broad credentials, or unbounded scheduled autonomy.</non_goals> Treat the harness as the kernel around an LLM OS: context is RAM, durable state is disk, tools are system calls, skills are programs, the scheduler is control, and evals are verifiers. Load `references/runtime-control-patterns.md` for matrices and schemas. Start guarded automation from `references/runtime-envelope-example.json` and validate it with `scripts/validate_runtime_envelope.py --strict`. ## Usage Template Provide: workflow, users, agent roles, environment, tools/connections, data sensitivity, delegated actions, cadence, throughput/SLA, failure history, and risk tolerance. ## Workflow <intake> Run the trace gate: the harness must be able to show what the agent saw, proposed, called, changed, and verified. Separate **Intent Plan** (human-reviewable source), **Compiled Contract** (validated runtime envelope), **Agent** (instructions/capabilities), **Environment** (network/files/credential broker), and **Session** (mounted context/eve...