agent-workflow

To onboard an AI agent without it inventing business rules, use a strict agent workflow protocol that enforces reading project memory and context files before making any changes. This prevents ambiguous implementations and scope drift by requiring the agent to gather existing documentation first.

The best way to structure AI agent workflows for feature implementation and bug fixes is to enforce task decomposition into atomic steps, group clarifying questions, and require plan confirmation with execution checkpoints. This standardized flow ensures disciplined development and prevents unapproved scope changes.

To stop an AI coding agent from drifting outside the project scope, apply explicit no-inference and no-drift rules within the agent workflow. These governance rules pause work automatically when missing dependencies, business decisions, or inconsistencies are detected during task execution.

Yes, a disciplined agent workflow requires specific project documentation formats, primarily a .docs/memory directory and a .docs/context directory. The agent reads these files during onboarding to understand project state and appends only completed facts to maintain documentation hygiene.

You should use a structured agent workflow protocol instead of direct prompting when resuming a project without context, implementing complex features, or fixing bugs. It is essential whenever you need to prevent scope drift, enforce documentation hygiene, and ensure technical sub-skills are invoked correctly.