Agent-Based Task Execution with Modular Components

Lead A modular agent system processes user requests through specialized components: a planner decomposes goals, executors handle tool use (e.g., code, web), and a critic validates outputs. This structure enables transparent, step-by-step task completion while maintaining loose coupling between components.

Details The planner receives the user request and generates a structured plan with subtasks, tool assignments, and success criteria. Executors: each executor (e.g., CodeExecutor, WebExecutor) receives a subtask, uses its designated tool to produce intermediate outputs, and returns structured results. The critic evaluates outputs against the plan's success criteria, providing feedback for refinement or signaling completion. All components communicate via standardized schemas (e.g., JSON plans/results), enabling independent updates.

Context This design mirrors modular AI architectures where separation of concerns improves maintainability and scalability. Planner/critic roles align with loose coupling allow swapping implementations (e.g., different LLMs for planning) without altering executor logic, as validated in recent modular agent frameworks.