Building AI Agents with Function Calling: Tools, Structured Output & Agent Loops

A developer published a technical guide on building AI agents with function calling, covering tool registries, structured output via JSON Schema, and agent loops. The guide walks through advanced patterns including ReAct, plan-and-execute, and self-correcting agents, plus parallel tool execution, memory and state management with SQLite, and production deployment via FastAPI and Docker. It closes with an evaluation framework and a production checklist spanning reliability, observability, security, and testing.

Building AI Agents with Function Calling: Tools, Structured Output & Agent Loops Master function calling for AI agents — from basic tool use to complex multi-step agents with planning, memory, and error recovery. Introduction Function calling transforms LLMs from text generators into agents that act . This guide covers production patterns for tool use, structured output, agent loops, and orchestration. Why Function Calling? | Without Tools | With Function Calling | | Text-only responses | Actions in the real world | | Hallucinated facts | Verified data from APIs | | No memory | Persistent state | | Single turn | Multi-step workflows | | Manual integration | Standardized interfaces | Part 1: Function Calling Fundamentals OpenAI Function Calling API Structured Output with JSON Schema Part 2: Building a Tool-Enabled Agent Tool Registry Agent Core Loop Part 3: Advanced Agent Patterns ReAct Pattern Reasoning + Acting Plan-and-Execute Agent Self-Correcting Agent Part 4: Multi-Tool Workflows Parallel Tool Execution Tool Chaining with Validation Part 5: Memory & State Management Conversation Memory Persistent State SQLite Part 6: Production Deployment FastAPI Agent Server Docker Deployment Part 7: Testing & Evaluation Agent Evaluation Framework Decision Framework Production Checklist Function Definitions - All tools have clear descriptions and examples - Parameter schemas are strict Pydantic validated - Tool responses are structured, not free text - Error responses include actionable information - Idempotent tools marked for safe retry Agent Reliability - Max iteration limits prevent infinite loops - Timeout handling for each tool call - Circuit breakers for external APIs - Graceful degradation when tools fail - State persistence for long-running agents Observability - Tool call logging input, output, latency, errors - Token usage tracking per step - Cost tracking per agent run - Success/failure rates by tool - Latency percentiles p50, p95, p99 Security - Input validation on all tool parameters - Sandbox for code execution tools - Rate limiting on external API calls - Authentication for sensitive tools - Audit logging for all actions Testing - Unit tests for each tool - Integration tests for agent flows - Golden test cases for common tasks - Chaos testing tool failures, timeouts - Regression tests in CI/CD Resources Function calling is the bridge between LLM reasoning and real-world action. Build robust tool interfaces, implement proper error handling, and always evaluate your agents on realistic tasks before deploying.