separation-of-concerns-skill

Enforcing strict separation of concerns across multi-step task orchestration prevents cross-contamination of reasoning, state, or side effects. Explicit phase boundaries isolate planning, observation, interpretation, execution, and review to maintain clean and auditable agent workflows.

Separation of concerns in AI agent orchestration is the practice of isolating reasoning, state, and side effects across distinct task phases. It enforces explicit boundaries between planning, observation, interpretation, execution, and review to keep complex workflows clean and auditable.

To enforce explicit phase boundaries when coordinating multiple AI agents, apply strict separation of concerns across all phases. This requires dedicated frontmatter naming and documented concern pairings to prevent data leakage between planning, execution, and review tasks.

The best way to keep multi-step task orchestration auditable is applying strict separation of concerns. Documenting concern pairings and execution plans while enforcing explicit phase boundaries prevents state leakage and ensures each sub-task remains clean and isolated.

Apply separation of concerns to an AI agent workflow when coordinating complex multi-step tasks across planning, observation, interpretation, execution, and review. It is essential whenever cross-contamination of reasoning or side effects threatens the reliability of your orchestration.

Yes, this separation of concerns approach works without external dependencies. It relies entirely on enforcing explicit phase boundaries and documenting concern pairings within your existing agent orchestration to isolate state and reasoning natively.