ai-agents-architect

Register tools with complete schemas and examples, then use a ReAct loop where the agent reasons, selects a tool, observes the result, and repeats. Always set a maximum iteration limit and surface tool errors explicitly to the agent.

ReAct interleaves reasoning and acting step by step, adapting after each observation. Plan-and-execute decomposes the full task upfront, executes each step, and replans only when results deviate, often using separate planner and executor models.

Set a hard maximum iteration limit on every agent loop before deployment. Combine this with tracing so you can inspect where the agent gets stuck, and surface tool errors so the agent can recover instead of retrying blindly.

Use multi-agent orchestration only when a single agent cannot handle the task's distinct responsibilities, such as separate planning and execution roles. Adding agents without justification increases coordination overhead and failure modes.

Tool overload degrades selection accuracy and confuses the agent. Curate a minimal tool set per task, write complete tool descriptions, and lazy-load expensive tools rather than exposing everything at once.