Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.

Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.

Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.

Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.

Use when the workflow lacks error handling, has been failing in production, or needs retry logic, fallback strategies, and circuit breakers.

Use when the workflow needs to self-correct, improve over time, or establish feedback loops and evaluation cycles.

Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.

Use when the user wants to tailor a workflow for a specific industry, domain, or vertical with specialized expertise, terminology, and guardrails.

Use when the workflow feels too complex, has accumulated cruft, or has redundant steps and overlapping tools that need consolidation.

Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project.

Use when the workflow feels over-engineered, has premature optimizations, unnecessary abstraction layers, or complexity beyond actual requirements.

Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing.

Use when you need maximum precision on a critical task — production deployments, security-sensitive code, financial calculations, or any work where mistakes are unacceptable.