How I Built an AI Agent with LLM Function Calling (and Avoided Unnecessary Tool Calls)

说明与用途

构建了一个使用LLM函数调用的AI代理，通过决策层避免不必要的后端调用，降低延迟并提高效率。

问题

The LLM was calling backend APIs on almost every user input, causing increased latency, unnecessary API calls, and inefficient system behavior.

结果

Reduced unnecessary tool calls, lowered latency, and improved response consistency in the AI-powered course recommendation system.

工作流

• 1. Receive user input
• 2. LLM generates intent and decision to call a tool
• 3. Orchestration layer evaluates decision
• 4. If tool call is needed, LLM gathers missing parameters via follow‑up questions
• 5. Execute backend API (tool) only when all parameters are present
• 6. LLM generates final response to user

证据

The model was calling backend APIs almost every time—even when it wasn’t necessary.

来源

Instead of blindly executing tool calls, the agent identifies missing information, asks follow‑up questions, and executes the function only when all parameters are available.

来源