| name | agentic-engineering |
| description | Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. |
Agentic Engineering
Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.
Operating Principles
- Define completion criteria before execution.
- Decompose work into agent-sized units.
- Route model tiers by task complexity.
- Measure with evals and regression checks.
Eval-First Loop
- Define capability eval and regression eval.
- Run baseline and capture failure signatures.
- Execute implementation.
- Re-run evals and compare deltas.
Task Decomposition
Apply the 15-minute unit rule:
- each unit should be independently verifiable
- each unit should have a single dominant risk
- each unit should expose a clear done condition
Model Routing
| Tier | Model | Use For |
|---|
| Fast/Cheap | Haiku 4.5 (claude-haiku-4-5) | Classification, boilerplate, narrow edits |
| Balanced | Sonnet 4.6 (claude-sonnet-4-6) | Implementation, refactors, most coding |
| Deep | Opus 4.8 (claude-opus-4-8) | Architecture, root-cause analysis, multi-file invariants |
Escalate model tier only when lower tier fails with a clear reasoning gap.
Session Strategy
- Continue session for closely-coupled units.
- Start fresh session after major phase transitions.
- Compact after milestone completion, not during active debugging.
Review Focus for AI-Generated Code
Prioritize:
- invariants and edge cases
- error boundaries
- security and auth assumptions
- hidden coupling and rollout risk
Focus review cycles on behavior and logic; rely on automated tooling for style enforcement.
Cost Discipline
Track per task:
- model
- token estimate
- retries
- wall-clock time
- success/failure
Escalate model tier only when lower tier fails with a clear reasoning gap.
Agent Development Lifecycle (ADLC)
Structured approach for building agent-powered features:
- Define — Write eval criteria and acceptance tests before implementation
- Build — Implement agent workflow with tool definitions (MCP preferred)
- Test — Run capability evals + regression evals, measure quality metrics
- Deploy — Canary rollout with observability; use feature flags for gradual enablement
- Monitor — Track success rate, cost, drift, and user feedback in production
Tool Integration
- Use MCP (Model Context Protocol) as the standard for agent tool access
- Define tools with precise schemas — vague descriptions produce vague tool calls
- Prefer server-side tool execution over client-side when security matters
Agent Security & Governance
- Agents are non-human identities: apply zero-trust principles (least-privilege, time-bounded credentials)
- Audit every tool call and decision in structured logs
- Define a governance policy: which actions require human approval?
- Review agent outputs for compliance before external delivery
Team Structure
- Optimal: 2–4 person pods with AI agent support
- Each pod member reviews agent output, not just code
- Measure team by business outcomes, not vanity metrics (lines of code, commits)
