How do you implement tool calling and function calling in AI agents?
Tool calling enables AI agents to interact with external systems, APIs, and data sources. Architecture: define a tool registry with descriptions, parameter schemas, and execution functions. The LLM receives tool descriptions, decides when and which tool to call based on the user request, generates structured parameters, and the runtime executes the tool and returns results. OpenAI function calling: define tools as JSON schema objects with name, description, and parameters. The model returns a toolcalls array with function name and arguments. Parse arguments, execute the function, and append the result as a tool message. Implementation: (1) Define tools with clear descriptions (the LLM uses descriptions to decide when to use each tool), (2) Implement parameter validation (validate LLM-generated parameters before execution), (3) Handle errors gracefully (return error messages to the LLM so it can retry or adjust), (4) Implement retry logic (for transient failures in tool execution). Tool design best practices: keep descriptions concise but specific, include parameter constraints and examples, group related operations into single tools with operation parameters, and implement rate limiting for expensive tools. Security: validate all tool inputs (prevent injection attacks), implement authorization checks (tool execution respects user permissions), sandbox tool execution (limit file system access, network access), and log all tool calls for audit. Advanced patterns: parallel tool calls (execute independent tools simultaneously), tool chaining (output of one tool feeds into another), dynamic tool registration (add tools based on context), and tool recommendation (suggest relevant tools before the user asks). Monitoring: track tool usage frequency, success rates, latency, and cost per tool call.