#

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.

source & further reading

dev.to — original article Linux creator tested artificial intelligence for faster memory management How to Build Customer-Support Retrieval Architecture: Query Limits That Hold Sharing my open source developer hub