agentic-engineering

To ensure reliable Copilot CLI agent execution, decompose complex development tasks into concise 15-minute agent units. This approach applies explicit input/output contracts, SQL state management, and eval-first verification criteria to guarantee deterministic autonomous outcomes.

Background Copilot agents fail on large features due to hidden state and ambiguous goals. You can prevent this by defining explicit input/output contracts declaring exact files and values, and enforcing fail-fast guardrails to surface uncertainties as BLOCKER messages instead of guessing.

Manage workflow state for background or fleet agents using SQL-based state management. Store workflow state in SQLite tables rather than in-memory conversation history to ensure reliable execution across multiple autonomous Copilot CLI agents.

Eval-first verification for autonomous agent tasks requires defining explicit success criteria before dispatching an agent. This technique ensures deterministic outcomes by enforcing fail-fast error handling and verifying that the 15-minute task units meet exact requirements.

Yes, you can use this agentic engineering approach for fleet-scale task dispatch. It is specifically designed for Copilot CLI contexts where tasks are dispatched to background or fleet agents, utilizing SQLite state management and explicit contracts for reliable autonomous execution.

The best way to avoid hidden state in Copilot CLI is to break work into 15-minute task units with explicit input/output contracts. Declare exact files, values, and SQL rows required and produced, eliminating reliance on implicit conversation context.