ai-agents-architect

To decide if your task requires an AI agent, assess the "Agent Tax" of latency, cost, and unpredictability against the complexity of your task. If a simpler LLM pipeline or chain suffices, skip the agent architecture.

Choosing between ReAct and Plan-Execute architecture patterns depends on your specific task requirements. Evaluate whether dynamic reasoning or structured planning better suits the complexity of your AI agent workflow.

Write clear, effective tool descriptions and manage tool count to improve AI agent reliability. Proper tool design ensures the LLM selects the right tools and reduces hallucinated tool calls during execution.

AI agents get stuck in infinite loops due to reasoning failures or tool output misinterpretation. Debug common agent failures by analyzing reasoning steps, validating tool schemas, and correcting plan abandonment.

Use a multi-agent architecture when task complexity exceeds a single agent's capacity. Apply a multi-agent decision framework to determine when multiple agents are necessary and select appropriate communication patterns.

Fix hallucinated tool calls in your LLM agent by optimizing tool schemas and refining descriptions. Clear tool design reduces LLM confusion and ensures accurate tool selection during agent execution.