Building AI Agents for Production: A Step-by-Step Guide
AI agents represent the next frontier of LLM applications. Unlike simple chatbots, agents can take actions, use tools, and work toward goals autonomously.
What Makes an Agent
An AI agent combines:
- A language model for reasoning and planning
- Tools (APIs, search, code execution) for taking actions
- Memory for maintaining context across interactions
- A loop for iterating toward a goal
Agent Architecture Patterns
ReAct: The model reasons about what to do, takes an action, observes the result, and continues.
Plan-and-Solve: The model creates a plan first, then executes steps sequentially.
Multi-agent: Multiple specialized agents collaborate, each handling different aspects of a task.
Building for Reliability
Production agents need:
- Error handling: What happens when a tool fails?
- Timeouts: How long should the agent try before giving up?
- Human-in-the-loop: When should the agent ask for help?
- Monitoring: How do you track what the agent is doing?
Deployment Considerations
Cost: Agent loops can be expensive. Cache results when possible.
Latency: Some agent architectures take multiple seconds per step.
Security: Limit what tools and data the agent can access.
Learning by Building
The best way to understand agents is to build one. Start with a simple research assistant that can search the web and synthesize findings. 212AY's Build with LLMs programme guides students through building production-ready agents.