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Agentic Coding Playbook ​

A practical playbook for teams: when to use AI agents, when humans should stay in the loop, and how to make agentic coding safer and more accountable.

Lan Nguyen Si | CC BY 4.0

Browse it with navigation and search on the agent-dx playbook site, linked from the repository README.

Core thesis ​

Agents may optimize. Humans must remain accountable. Systems must enforce the difference.

This package focuses on the human and organizational side of agentic coding:

  • when to use agents
  • when not to use agents
  • what kinds of review are needed
  • how to reduce operational and quality risk

Main playbooks ​

PLAYBOOK-EN.md: Executive playbook (English)

PLAYBOOK.md: Executive playbook (Deutsch)

One-page overview ​

English: Agentic Coding Playbook

Deutsch: Agentic Coding Playbook

Additional reference material ​

For teams that want a clearer model for review depth and implementation quality:

These companion documents describe the difference between no review, normal review, and rigorous review, and propose a practical Role + Skill + Standard model for implementation agents.

For teams remediating dependency CVEs in an npm lockfile specifically:

Operational artifacts ​

For teams that want to apply this model directly in a repository, this package also contains reusable artifacts:

  • templates/: specs with evals, task slicing, agent tasks, risk classification, review reports, and PR descriptions
  • policies/: example risk tiers, protected files, approval rules, and quality gates
  • skills/: reusable agent role prompts (task slicer, implementation agent, reviewer, skeptic, release gate)
  • examples/: placeholder for stack-specific and workflow-specific examples; none are included yet

These artifacts are tool-agnostic and bilingual. Adapt them to your harness, CI system, and team workflow; they are not an enforcement layer on their own.

Contributing ​

This is a living document. Feedback, corrections and additions are welcome via Issues and Pull Requests.

License: CC BY 4.0