CODING AGENTS PLAYBOOK
Coding Agents Playbook: A Practical SOP to Ship Faster (Without Losing Control)
A copy-paste, production-minded playbook for using AI coding agents responsibly: Spec → Plan → Patch → Proof → PR, with guardrails, templates, prompts, CI gates, and an end-to-end walkthrough.
What is a coding agent?
A coding agent is an AI-assisted workflow (usually inside an IDE or repo tool) that can plan, edit code across files, run tests, and iterate on feedback—like a high-speed junior engineer that needs constraints and review.
The 5-step workflow (copy/paste)
- Spec: define goal, scope, constraints, and acceptance criteria.
- Plan: agent proposes files, approach, risks, and test strategy.
- Patch: implement the smallest reviewable diff.
- Proof: run checks + show evidence (tests/lint/typecheck/bench).
- PR: summarize changes, risks, and verification; iterate fast in review.
Why you need a playbook (not just an agent)
“Using a coding agent” can mean anything from autocomplete suggestions to a tool that edits your repo and proposes multi-file changes. The common failure mode is not that the agent writes “bad code”— it’s that the workflow becomes unreviewable, non-auditable, and unsafe. If a human cannot quickly answer what changed and why, agent speed turns into delivery risk.
This post gives you a production-minded standard operating procedure (SOP) that works for solo developers, startups, and teams: predictable prompts, tight scopes, reviewable diffs, “proof” requirements, and CI gates that catch typical agent mistakes (security regressions, phantom APIs, noisy refactors, and missing tests).
Choose your lane: Solo, Team, or Regulated
The same workflow applies everywhere. The difference is how strict your gates are.
Lane A: Solo builder
- Plan gate for anything > 30 lines or > 2 files.
- Proof: tests + lint at minimum.
- Diff cap: ~500 lines unless intentional refactor PR.
Lane B: Team shipping weekly
- Plan gate for most tasks; PR template required.
- Diff cap: ~300 lines unless approved.
- Branch protection + required checks + CODEOWNERS.
Lane C: Regulated / enterprise
- Threat notes for auth/security changes.
- Mandatory security scans + SBOM tracking.
- Feature flags, staged rollouts, rollback SOP.
The North Star: Agent output must be auditable
Treat coding agents like high-speed junior engineers. They’re productive and fast, but they’ll also: over-edit, invent APIs, add unnecessary abstractions, or “polish” unrelated code. The fix is not banning agents—it’s standardizing what “done” means.
Definition of Done (Agent Edition)
- Spec exists (goal, scope, constraints, acceptance criteria).
- Plan approved (files touched, approach, risks, tests).
- Minimal diff (fewest files, no drive-by refactors).
- Proof provided (commands + results + artifacts).
- Risk notes listed (edge cases, limitations, follow-ups).
The core workflow: Spec → Plan → Patch → Proof → PR
1) Spec (Human): write constraints, not novels
Most agent failures are specification failures. Your spec should be short, strict, and testable. You’re building guardrails so the agent can go fast without going wide.
Ticket / Spec Template (copy/paste)
## Goal
- What outcome must exist after merge?
## In scope
- Exact behaviors/features to implement.
## Out of scope
- What must NOT change (APIs, schemas, deps, formatting, etc.)
## Constraints
- Style/conventions:
- Performance:
- Security:
- Backward compatibility:
## Acceptance criteria
- Tests that must pass:
- Edge cases to cover:
- Expected outputs / screenshots / API examples:
## Proof required
- Commands to run and report:
2) Plan (Agent): demand a file-level plan before code
The plan gate is your best defense against scope creep. Require the agent to list files, steps, risks, and tests. If the plan looks too big, you shrink it before the first line of code is written.
Agent Plan Prompt (copy/paste)
You are an engineering assistant. Before writing any code, produce a plan:
1) Files to touch (paths)
2) Step-by-step approach
3) Risks/unknowns and how you’ll validate them
4) Test plan (what to add/modify)
5) Proof you will provide (commands + artifacts)
Constraints: minimal diff; touch fewest files; no new deps without approval; no unrelated refactors.3) Patch (Agent): enforce minimal-diff discipline
The single most important team rule: one PR = one intention. Agents love “cleanup.” Your playbook should forbid it unless the task is explicitly a refactor.
Minimal-diff rules (recommended)
- No dependency changes without explicit approval.
- No mass formatting changes unless PR is formatting-only.
- Prefer additive changes over rewrites when possible.
- Diff cap: 300 lines (teams) or approval required.
- Touch the fewest files possible.
Change Summary format (required)
- What changed (2–5 bullets)
- Files touched (list)
- Tests run (commands + results)
- Risks / follow-ups (short list)
4) Proof (Agent + CI): require evidence, not confidence
Agents can generate convincing code that fails in edge cases. Proof is your countermeasure. Your baseline proof should include lint/typecheck/tests—and artifacts when UI or API behavior changes.
Proof checklist
- Unit tests added for new logic or regression.
- Relevant test suite passes (and/or full suite if feasible).
- Lint + formatting checks pass.
- Typecheck passes (if applicable).
- API: example requests/responses; UI: screenshots.
5) PR (Human + Agent): review fast, iterate faster
Your PR is the audit record. Require a short PR description that makes review trivial. The agent can help write the PR description, but a human owns correctness and intent.
PR Template (copy/paste)
## Summary
- (2–5 bullets) what changed and why
## Scope
- In scope:
- Out of scope:
## Files touched
- path/to/fileA
- path/to/fileB
## Verification / Proof
- Commands run:
- ...
- Results:
- ...
## Risks / Notes
- Edge cases:
- Limitations:
- Follow-ups:
## Screenshots / API examples (if applicable)
- ...Playbook Kit: AGENTS.md + Prompt Pack + Quality Gates
The fastest way to make agents reliable is to encode your rules into the
repo. Add an AGENTS.md file at the root so every agent run
inherits the constraints.
AGENTS.md Template (drop into your repo)
# Agent Rules (AGENTS.md)
## Non-negotiables
- Minimal diff: touch the fewest files possible.
- No drive-by refactors. No renames/reformats unless requested.
- No new dependencies without explicit approval.
- Do not change public APIs or response schemas unless requested.
- Do not log secrets or sensitive data.
## Required process
1) Propose a plan (files, steps, risks, tests) before coding for tasks >30 lines or >2 files.
2) Add/modify tests for logic changes and bug fixes (regression tests required).
3) Provide Proof: list exact commands run and summarize results.
## Security baseline
- Parameterized queries only; no string concatenation for SQL.
- Validate and sanitize inputs at boundaries.
- Reuse existing auth/permission middleware; do not implement custom auth.
- Avoid dynamic code execution (eval, unsafe deserialization).
- Never commit secrets.
## Output format
- Plan → Patch summary → Proof (commands + results) → Risks/follow-ups.
Prompt Pack (copy/paste)
Use these prompts as standard entry points. They’re designed to reduce common agent failure modes: phantom APIs, scope creep, missing tests, noisy diffs, and security regressions.
Prompt 1: Bug fix with regression test
Reproduce the bug from these steps/logs. Write a failing regression test first.
Then implement the smallest fix to make it pass.
Constraints: minimal diff; no unrelated refactors; no new deps.
Finish with Proof: commands run + results.Prompt 2: Refactor safely
Refactor this module to improve readability/maintainability while preserving behavior.
Do not change public APIs. Keep diff small and staged.
Add/adjust tests if needed. Provide Proof and note any tradeoffs.Prompt 3: Add tests for existing code
Add unit tests to cover these functions and edge cases.
Do not change production code unless necessary for testability (and justify if you do).
Provide Proof: exact test commands and summary.Prompt 4: Security review
Review these changes for security issues: auth/permissions, injection, secrets, logging, input validation.
List findings by severity and propose minimal patches + tests.
Do not introduce new auth patterns; reuse existing middleware.Prompt 5: Performance sanity
Assess performance risks in this change (hot paths, N+1, allocations, I/O).
Suggest low-risk improvements. If changes are needed, keep them minimal and add benchmarks if applicable.Prompt 6: PR reviewer mode
Act as a strict reviewer. Check correctness, edge cases, tests, style consistency, and scope creep.
Flag any unnecessary edits. Suggest concrete improvements and missing tests.Quality Gates (policy posture)
- No merge if acceptance criteria aren’t explicitly met.
- No merge for logic changes without tests.
- No merge for dependency additions without approval.
- No merge for auth/security changes without risk notes + reviewer sign-off.
- Diff cap: >300 lines requires explicit reviewer approval (teams).
End-to-end walkthrough: Rate limiting a login endpoint (Spec → PR)
Here’s a complete example to show the playbook in action. The point is not the specific tech— it’s the workflow and the evidence.
Step 1 — Spec (ticket)
Goal:
- Add rate limiting to POST /api/login.
In scope:
- Limit to 5 attempts per 15 minutes per (IP + username).
- Use existing Redis connection/utilities.
Out of scope:
- Do not change response schema or status codes other than rate-limit response.
- Do not add new dependencies.
Constraints:
- Security: do not log passwords; avoid leaking whether username exists.
- Compatibility: existing clients must continue working.
- Style: follow existing middleware patterns.
Acceptance criteria:
- Unit tests:
- allows up to 5 attempts in window
- blocks 6th attempt with appropriate response
- resets after 15 minutes
- Lint/typecheck/tests pass.
Proof required:
- npm test (or equivalent)
- lint/typecheck commandsStep 2 — Plan (agent plan output)
- Files to touch: login route handler, middleware/util for Redis rate limit, test file.
- Approach: add a small rate-limit check before auth logic; key = hash(IP + username); TTL 15m.
- Risks: avoid username enumeration; ensure key normalization; ensure Redis errors fail open/closed intentionally.
- Tests: add unit tests with fake timers; mock Redis or use in-memory test adapter.
- Proof: run lint, typecheck, tests; report outputs.
Step 3 — Patch (minimal diff principles)
The agent should implement the smallest change that satisfies the spec: add a tiny limiter utility and wire it into the login route. No broad refactors. No renaming spree. No dependency additions.
Step 4 — Proof (commands + results)
Commands run:
- npm run lint
- npm run typecheck
- npm test
Results:
- lint: pass
- typecheck: pass
- tests: pass (including new rate limit tests)Step 5 — PR (audit record)
Summary:
- Added Redis-backed rate limiting for POST /api/login (5 attempts / 15 min per IP+username).
- Added unit tests to verify limit enforcement and window reset.
Files touched:
- src/api/login.ts
- src/lib/rateLimit.ts
- tests/login.rateLimit.test.ts
Verification:
- npm run lint (pass)
- npm run typecheck (pass)
- npm test (pass)
Risks/Notes:
- If Redis is unavailable, behavior follows existing error strategy (documented in code).
- Follow-up: consider global IP-only limiter for distributed attacks.Notice what makes this “agent-safe”: strict scope, predictable file touch set, tests, proof, and explicit notes. That’s the difference between shipping fast and shipping chaos.
CI & enforcement: the control plane that keeps quality stable
Good intentions don’t scale. Enforcement does. Even lightweight CI gates dramatically reduce agent-induced risk. Your goal is to make it harder to merge unsafe output than it is to do the right thing.
Branch protections (recommended)
- Require PR reviews (1–2 reviewers).
- Require status checks: lint, tests, typecheck.
- Require CODEOWNERS approval for sensitive paths (auth, payments, infra).
- Disallow force-push to main.
Automated checks (recommended)
- Secret scanning (prevent accidental key commits).
- Dependency vulnerability scan (SCA).
- Static analysis (SAST) for common issues.
- Optional: formatting gate to prevent noisy diffs.
Policy for “sensitive changes”
- Auth/permissions: add risk notes + tests + reviewer sign-off.
- Data migrations: include rollback plan and verification queries.
- Payments/billing: include golden tests and edge-case enumeration.
- Security fixes: document threat scenario and mitigation.
Common mistakes (and the guardrail that prevents each)
How to measure if coding agents are actually helping
Agent adoption can feel fast while increasing rework. Track outcomes, not vibes: lead time, review time, CI failures, and revert rate. If those trend worse, tighten your gates.
Core delivery metrics
- Lead time to merge
- Review time per PR
- CI failure rate
- Revert/hotfix rate
- Bug count linked to recent merges
Quality and maintainability
- Diff size distribution
- Test coverage change (targeted areas)
- Security findings trend
- Incidents tied to sensitive modules
The goal isn’t maximum code output. It’s higher throughput with stable quality. A good playbook makes agent output predictable; a great playbook makes it measurably safer.
FAQ (AEO-friendly)
How do you use coding agents safely in production?
Use a strict workflow (Spec → Plan → Patch → Proof → PR), enforce
minimal diffs, require tests for logic changes, and gate merges with
CI checks. Add repo-level rules in AGENTS.md so agents
inherit constraints.
What should go in an agent prompt for refactoring?
Include the goal, the files in scope, what must not change (public APIs), a minimal-diff constraint, and a test plan. Require proof: lint/typecheck/tests and a change summary.
Do coding agents replace engineers?
No. They accelerate implementation and iteration, but humans must define intent, validate correctness, and own accountability. Think of agents as fast assistants, not autonomous owners of your system.
What are the best guardrails for LLM-generated code?
Minimal-diff policy, dependency gate, tests required for logic changes, proof requirement, security baseline checklist, and CI enforcement (branch protection + required checks + CODEOWNERS for sensitive paths).
How do you prevent agents from making huge, noisy changes?
Force a plan gate, cap diff size, ban unrelated refactors, enforce formatting in CI, and require a file list in the plan and PR summary. Reviewers should reject scope creep aggressively.
When should you avoid using a coding agent?
When requirements are ambiguous, when the work involves novel architecture decisions, or when changes touch high-risk domains (auth, payments, safety-critical logic) without a strong spec and tests. In those cases, use agents as reviewers or test writers first.
What’s the fastest way to improve agent accuracy in your repo?
Add AGENTS.md rules, create scripts like
scripts/test and scripts/lint, and maintain
a small architecture doc that explains where key modules live.
Consistency boosts agent quality.
How do you measure ROI from coding agents?
Track lead time, review time, CI failures, hotfix/revert rate, and bug counts tied to recent merges. If speed increases but rework increases, tighten gates and improve prompts/templates.
Bottom line: The playbook is the product
Coding agents are powerful, but power without constraints is noise. If you want speed and control, standardize: a spec template, a plan gate, minimal diffs, proof requirements, and CI enforcement. Do that, and agents become a true force multiplier—not a risk multiplier.