Building AI Agents: A Practical Guide for Software Engineers
What Are AI Agents?
An AI agent is a system that uses an LLM to reason about tasks, make decisions, and take actions autonomously. Unlike a simple chatbot that responds to messages, an agent can plan multi-step workflows, call external tools, and adapt based on results.
The Core Loop
Most agents follow a variation of the ReAct (Reasoning + Acting) pattern:
- Observe — Receive input or observe the current state
- Think — Use the LLM to reason about what to do next
- Act — Execute a tool call or take an action
- Observe — Check the result and decide if the task is complete
This loop continues until the agent determines the task is done or hits a maximum iteration limit.
Tool Calling Is the Foundation
Agents are only as useful as the tools they can access. Common tools include:
- Web search and URL fetching
- Database queries
- API calls to external services
- File system operations
- Code execution
The key is defining clear tool schemas so the LLM knows what each tool does, what parameters it accepts, and what it returns.
Multi-Agent Systems
For complex tasks, multiple specialized agents can collaborate. One agent might handle research, another writes code, and a third reviews the output. Orchestration patterns include:
- Sequential — Agents work in a pipeline
- Parallel — Agents work simultaneously on different subtasks
- Hierarchical — A manager agent delegates to worker agents
Error Recovery
Production agents need robust error handling. Tools fail, APIs timeout, and LLMs sometimes make bad decisions. Good agent design includes retry logic, fallback strategies, and graceful degradation.
Getting Started
Start with a single-tool agent (like a web researcher), then progressively add tools and complexity. The AI Agents module walks through this progression from first tool caller to multi-agent systems.