Agentic Engineering

Agentic Engineering

<skill_contract> <input>An engineering objective, inspectable repository or workflow, acceptance criteria, permissions, risk, and state location.</input> <output>The smallest sufficient model-native process with bounded autonomy, evals, recovery, and verified write-back.</output> <done>Fresh task and adoption evidence support the observable end state without crossing authority boundaries.</done> <non_goals>Agent complexity for its own sake, premature multi-agent topology, or unverified knowledge promotion.</non_goals>

An agent is a stateful engineering process, not a prompt. Its quality ceiling is the combination of objective, context, tools, taste/evaluation, permissions, recovery, and feedback latency.

Usage Template

Provide: engineering objective, repository/workflow, users, acceptance criteria, constraints, permissions, risk, current evidence, and durable state location.

Workflow

<intake>

1. Inspect repository guidance, code, tests, state, and current failure before proposing architecture.
2. Define the observable end state, non-goals, owner, budget, and review bandwidth. Put code and non-code constraints in one versioned, reviewable intent surface; chat history alone is not the shared plan.
3. Run the adoption gate: use an agent only when ambiguity/adaptation outweigh orchestration, verification, and maintenance cost. Prefer deterministic code for stable transformations.

</intake>

<unknowns_gate>

Map unknowns into: known, probeable from tools/files, testable by prototype, and externally blocked. Probe boundary/interface unknowns before implementation. Return NEEDS_INPUT only when a missing business decision, permission, or irreversible tradeoff cannot be discovered locally; otherwise label assumptions and test them.

</unknowns_gate>

<execute>

1. Write the macro action: trigger -> objective -> inputs -> constraints -> artifact -> verifier -> state -> stop/recovery.
2. Define quality with domain-specific examples, anti-examples, guardrails, and cheap checks; generic “good quality” is invalid.
3. Decompose into the fewest independently verifiable units with one owner each. Probe representative units and shorten the task horizon until every delegated unit has a cheap verifier and an evidence-backed reliability threshold; do not delegate a large refactor as one zero-shot goal.
4. Select the lowest sufficient topology: one-shot for one bounded action, loop-engineering for temporal correction, graph-engineering for explicit dependency width and joins, and agent-teams-command only when distinct worker processes and integration ownership add value.
5. Route by capability (reasoning, tool use, latency, context, modality, cost) and runtime policy; record route, latency, cost, and verifier result while keeping vendor/model names out of durable contracts.
6. Treat model text and tool arguments as proposals. Normalize the runtime termination_reason into complete, tool request, checkpoint/truncation, or escalation; only host code may execute tools or decide continuation.
7. Establish harness controls: least privilege, tool schemas, timeouts, observability, checkpoints, idempotency, staged effects, and rollback. Compile the reviewed intent into a validated runtime envelope and bind the plan and envelope hashes in durable state.
8. Run a thin loop: understand -> plan -> smallest change -> targeted test -> inspect diff/state -> broader check.
9. Use independent evaluation or adversarial review for consequential logic, interfaces, and claims.
10. Remove temporary scaffolding, duplicate abstractions, and context that no longer changes decisions.
11. Write back only reusable, verified deltas. Promotion into skills/SOPs requires repeated support or local verification plus a cheap objective check.

Human approval is mandatory before production, publication, spending, destructive mutation, credentials, policy, or other delegated external action. Prepare rollback before crossing that boundary.

</execute>

<evaluate>

Compare the result with acceptance criteria, custom evals, tests, diff scope, security/permission boundaries, and user workflow. Check both task success, task-horizon calibration, shared-plan fidelity, and adoption cost. A large reasoning trace is not evidence; receipts are.

</evaluate>

<retry_policy>

max_attempts: 3 per failure class. Retry only after updating the diagnosis and changing strategy, input, or tool. Stop on repeated signature, expanding blast radius, exhausted review bandwidth, or NO_PROGRESS.

</retry_policy>

<state_contract>

Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus objective/non-goals, shared-plan and runtime-envelope hashes, decisions, probes, active files, normalized termination reason, tool receipts, diff, eval results, permissions, approval, rollback point, and write-back candidates. Version checkpoints at phase boundaries.

</state_contract>