How do AI agents use tool calling to interact with external systems?

Tool calling enables AI agents to invoke external functions, APIs, and services during reasoning. The LLM receives a tool schema describing available functions with parameter types and descriptions, decides when to call a tool based on the user query, generates structured arguments, and processes the tool output to formulate a response. Implementation: define tools as JSON schemas with name, description, and parameter definitions. The agent loop: (1) LLM receives query and available tools, (2) LLM decides to call a tool and outputs structured arguments, (3) application executes the tool call, (4) result is fed back to the LLM, (5) LLM decides to call another tool or generate a final response. Parallel tool calling allows multiple independent tool calls in a single step, reducing latency. Error handling: tools should return structured error messages that the LLM can interpret and retry with corrected parameters. Security: validate all tool arguments before execution, implement rate limits per tool, use least-privilege credentials, and log all tool invocations. Best practices: keep tool descriptions concise but informative, provide parameter examples, limit to 10-20 tools per agent (more causes decision confusion), and group related tools into specialized agents.