What Are AI Agents? An Introduction to Multi-Agent Systems

"AI Agent" has become a buzzword, but the core idea is simple: an agent = an LLM (like Claude) + tools (file reading, web search, code execution) + a loop that keeps running until the task is complete.

Chatbot vs Agent

• Chatbot: one question → one answer. No tools, no multi-step actions.
• Agent: given a goal, the model decides for itself which tools to use and when, looks at the results, and plans the next step — until the goal is achieved.

The Basic Structure of an Agent Loop

1. Give the model a goal + the available tools
2. The model decides: "I need to call tool X"
3. The tool executes, and the result goes back to the model
4. The model looks at the result and decides: call another
   tool, or give the final answer?
5. Repeat steps 2-4 until the task is complete

This is exactly the loop running behind tools like Claude Code — the agent reads a file, decides whether to edit it, runs a test, then makes its next decision based on the result.

Multi-Agent Systems

When a task is too large for one agent, multiple specialized agents are used instead — like a "researcher" agent that gathers information, and a "writer" agent that turns that information into a final output. Each agent has a focused role, and a "coordinator" divides the work between them.

• ✅ Benefit: complex tasks can be parallelized, and each agent performs better within its own domain.
• ⚠️ Trade-off: more agents mean more cost and coordination complexity.

Key Takeaways

• Agent = LLM + Tools + a Decision Loop, not just a chatbot response.
• An agent decides for itself when to use which tool, and plans its next step from the results.
• Multi-agent systems break large, complex tasks into specialized roles.