Establishing Agent Governance

Working with AI agents on enterprise codebases requires moving away from the 'blank slate' approach of toy apps. Instead, treat the agent as a digital intern that requires clear mentorship, boundaries, and context.

Workspace and Rule Hierarchy

  • Break Repository Walls: Configure your workspace to include multiple repositories (frontend, backend, shared packages) simultaneously. This allows the agent to perform cross-repo updates, such as synchronizing data model changes with corresponding TypeScript interface updates.
  • Implement Rule Hierarchies: Use a tiered system to enforce coding standards. Apply global rules (e.g., line length) in home directory configuration files, project-specific rules in root-level .agents/rules directories, and hyper-local context via README files within specific subdirectories.

Security and Guardrails

  • Contain the Blast Radius: Use security presets that require manual review for terminal commands. Build an allowlist of commands incrementally rather than upfront. Enable sandbox modes to restrict destructive operations and unauthorized network calls.
  • Cloud Isolation: Ensure your development environment is strictly decoupled from production systems to prevent accidental data exposure or modifications by the agent.

Interactive Planning and Context

  • Voice-First Brainstorming: Avoid the limitations of typing when defining complex requirements. Use native voice input to dump messy, high-context architectural problems and legacy quirks, simulating a conversation with a senior engineer.
  • The Interrogation Loop: Use the /grill-me command to force the agent to challenge your assumptions. By letting the agent analyze the codebase and ask sharp questions about fuzzy requirements, you can identify architectural gaps before a single line of code is written, significantly reducing future debugging time.