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/events/state). Define one auditable control path for high-risk intent and final joins. Model output is never execution authority.

</intake>

<unknowns_gate>

If state ownership, credential scope, external side effects, retention, or approval authority is unclear, return NEEDS_INPUT. Probe tools with read-only discovery where possible; unknown side effects default to denied.

</unknowns_gate>

<execute>

1. Pass Four-C: Context truth/retrieval, Connections scoped accounts/APIs, Capabilities versioned skills/scripts/evals, Cadence trigger/receipt/anomaly/stop.
2. Compile the reviewed intent plan into a versioned runtime envelope. Validate plan hash, tool_execution_owner: host, filesystem/network/secret boundaries, output cardinality, legal no-op, budgets, approvals, audit paths, and rollback before execution.
3. Map runtime: stored program, control unit, hot context, durable disk, event bus, I/O tools, verifier, and garbage collector.
4. Choose the lowest-context primitive: deterministic script/hook, skill, static Graph, connector, dynamic workflow, or agent team. Load capabilities lazily. Graph Engineering owns dependency semantics; the harness owns the ready queue, leases, duplicate delivery, concurrency, and executor health.
5. Define each tool as a narrow host-owned system call with purpose, explicit inputs, bounds, timeout, idempotency, failure path, evidence, and audit location. Validate model-proposed arguments before dispatch.
6. Enforce zero trust and least privilege in the environment, not only prose: bind access to task, resource, operation, and time; use exact network allowlists and opaque secret handles; never expose raw credentials to model context. Stage and vet writes before external commit.
7. Normalize each model termination_reason into complete, tool request, checkpoint/truncation, refusal/error, or unknown. The host decides whether to execute, continue, checkpoint, or escalate; success prose cannot override the control signal or verifier.
8. For delegated action require mandate, scope, limit, preview, receipt, and rollback. Human approval governs irreversible/shared/financial/published/credentialed actions.
9. Define allowed output types, maximum external outputs, and a verifiable NO_OP condition. Quiet execution is success only when eligibility was checked and no side effect occurred.
10. Add deterministic feedback (tests, lint, LSP, policy checks) outside context when possible; add independent evaluator/red team for high-risk semantic output.
11. Persist an append-only session event log and checkpoint; define alerts, fallback, incident response, cleanup, permission review, and stale-context/rule review.
12. For scheduled work define Trigger, Context, Steering, Receipt, budget, stop, recovery, and executor health. A schedule firing is not task success.
13. For Graph execution, persist node/edge/join transitions before releasing successors, make delivery idempotent, recover from the last verified checkpoint, and test permission denial, worker loss, duplicate events, and compensation without relying on in-memory scheduler state.

</execute> <evaluate>

Threat-model normal, denied, timeout, partial-write, stale-context, duplicate-trigger, compromised-input, evaluator-disagreement, and rollback paths. Verify permissions with actual environment boundaries and run an independent end-to-end trace. Adoption fails if operators cannot inspect or recover the system.

</evaluate>

<retry_policy>

max_attempts: 3 per tool/failure class. Retry only idempotent or compensated actions after changing diagnosis/strategy. Use exponential delay for transient dependencies. Stop on repeated signature, permission denial, ambiguous side effect, or NO_PROGRESS.

</retry_policy>

<state_contract>

Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus intent-plan hash, compiled-contract hash, agent/environment/session versions, context manifest, normalized termination reason, tool/permission/credential-lease registry, output quota, no-op receipt, event offsets, approvals, evals, alerts, recovery point, and rollback receipts. State transitions are auditable and replayable.

</state_contract>

Failure Protocol

• NEEDS_INPUT: ownership, effect, retention, or approval authority is ambiguous.
• BLOCKED_PERMISSION: deny the call and continue with a safer read-only path when useful.
• BLOCKED_DEPENDENCY: checkpoint, back off, and expose executor health.
• VERIFY_FAILED: trace, eval, guardrail, or rollback test fails; block autonomy escalation.
• NO_PROGRESS: changed attempts repeat the signature. max_attempts: 3.
• BUDGET_STOP: stop scheduler/workers, checkpoint, and emit a recovery receipt.

Output Contract

Return status, result (runtime architecture and controls), evidence (trace/eval/failure tests), unknowns, and next_action including approval or rollback.

Edge Cases

• A connector exposes broad account access for a narrow task: create a scoped proxy/allowlist or keep the workflow manual; instructions alone are insufficient.
• A model emits plausible success text with a tool request: validate and execute the tool in host code, then verify its result; do not treat the text as task completion.
• A maintenance run finds no eligible change: emit a verified NO_OP receipt and zero external outputs rather than creating notification or PR noise.
• The scheduler ran on time but the state store was stale: block mutation, mark the run failed, and recover from the last verified checkpoint.
• A Graph worker completes twice after lease expiry: deduplicate by node/run identity and release successors only from one verified transition.

Success Metrics

• Every delegated action is bounded, observable, independently verifiable, and recoverable.
• Runtime state can be replayed across sessions without relying on chat history.
• Autonomy level follows measured reliability and operator review capacity.

Quality Gates

• Agent, Environment, Session, and serial control ownership are explicit.
• Intent plan and compiled runtime envelope are hash-bound and pass strict validation.
• Tool effects are enforced by real permissions and contracts.
• Termination routing, secret isolation, output limits, and legal no-op are host-enforced.
• Independent eval, approval, manual fallback, and rollback match risk.
• Schedules have executor receipts, hard budgets, stop, and recovery.
• Maintenance includes cleanup, permission audit, and rule half-life.

</skill_contract>