Agentic Loops: Multi-Turn Reasoning and Tool Orchestration
Design agent loops that enable LLMs to reason iteratively, call tools, incorporate results, and refine decisions.
An agentic loop is a repeating cycle: LLM receives a user goal, reasons about available tools, calls one or more tools, receives results, incorporates them into its reasoning, and either: returns a final answer (goal achieved) or calls more tools (goal not yet achieved, needs more information). This is fundamental to autonomous AI agents. A well-designed loop includes: clear goal definition, tool availability discovery, reasoning transparency (the LLM explains why it's calling each tool), error handling and recovery, iteration limits to prevent infinite loops, and convergence criteria. Unlike one-off tool calls, agentic loops enable complex multi-step tasks like planning, problem-solving,…
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