ai-agents-architect

To design autonomous AI agents that use tools reliably, define control loops like ReAct, implement structured tool registries with schemas, and add iteration limits with guardrails to handle tool errors gracefully.

Multi-agent orchestration coordinates multiple AI agents to handle complex workflows, justified when tasks require distinct roles, providing clear traceability and predictable behavior across coordinated steps and shared memory systems.

Debug unpredictable agent failures during function calling by evaluating failure modes, implementing control loop boundaries like ReAct, and designing guardrails that trigger graceful degradation when tool invocations encounter errors.

You can build a single agent workflow with a ReAct loop and tool registry for predictable tasks, using multi-agent orchestration only when justified by complex requirements needing distinct roles and clear traceability.

ReAct loops for agent architecture require strict iteration limits and guardrails to prevent infinite reasoning cycles, needing failure-mode evaluation to ensure graceful degradation when tool use encounters unexpected errors.