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.