- name
- ai-first-engineering
- description
- Engineering operating model for teams shipping with AI-assisted code generation. Process shifts, architecture requirements, code review and testing standards. Trigger phrases: ai-first engineering, ai-assisted teams, agent code generation, ai team process, shared responsibility review.
- metadata
- {"clawdbot":{"emoji":"🛠️","requires":{"bins":[],"env":[]},"os":["linux","darwin","win32"]}}
AI-First Engineering
Engineering operating model for teams where AI agents generate a large share of implementation output. Adapted from everything-claude-code by @affaan-m (MIT).
Quick Start
- Invest in planning quality — ambiguous specs cause AI-generated code to fail; write clear acceptance criteria first
- Raise eval coverage — AI code requires higher test standards; regression coverage mandatory for touched domains
- Shift review focus — review for behavior, security, data integrity, failure handling; let automation handle style
- Design agent-friendly architecture — explicit boundaries, stable contracts, typed interfaces, deterministic tests
- Evaluate hiring signals — decomposition skill, measurable criteria definition, prompt quality, risk control discipline
Key Concepts
- Planning > Speed: Clear specs + good evals trump fast typing. AI can implement fast; humans must specify clearly.
- Automation is the baseline: Style, formatting, lint issues are solved by automation, not review.
- Architecture matters more: Implicit conventions break AI systems; use explicit boundaries and typed interfaces.
- Test coverage is non-negotiable: Generated code needs regression coverage for every touched domain.
- Shared responsibility: AI generates; human reviews for risk (security, data integrity, rollout safety); human refines when needed.
Common Usage
Code review in AI-first teams — focus on:
Behavior regressions: Did the change break existing functionality?
Security assumptions: Input validation, permission checks, sensitive data handling
Data integrity: Constraints, rollback safety, concurrent access
Failure handling: Network calls, database errors, timeouts, degraded modes
Rollout safety: Feature flags, backward compatibility, canary deploy strategyArchitecture for AI teams:
- Explicit boundaries between modules (not implicit conventions)
- Stable contracts (typed interfaces, documented behavior)
- Deterministic tests (no flaky tests — AI can't debug intermittent failures)
- Clear error paths (AI struggles with ambiguous error handling)
Testing standard raise:
- Regression coverage for every touched domain (required, not optional)
- Explicit edge-case assertions (AI may miss corner cases)
- Integration checks for interface boundaries (behavior across module lines)
Hiring Signals for AI-First Engineers
Strong signals:
- Decomposes ambiguous work cleanly → clear, testable units
- Defines measurable acceptance criteria → no scope creep, clear done condition
- Produces high-signal prompts and evals → AI generates better code from better specs
- Enforces risk controls under delivery pressure → doesn't skip security or testing for speed
Weak signals:
- "Move fast and break things" mindset
- Writing code without clear specs or acceptance criteria
- Skipping regression tests to save time
- Vague PR descriptions ("fixed bugs," "refactored stuff")
References
references/process-shifts.md— detailed planning, evals, review guidancereferences/architecture-guide.md— designing systems for AI code generationreferences/testing-standards.md— regression coverage, edge-case testing, integration checks