AI Agents Explained
An AI agent is an LLM inside a loop that can choose actions, use tools, observe results, and continue until it reaches a goal.
What it is
A normal chatbot answers once. An agent can search, calculate, retrieve documents, call APIs, update state, and decide what to do next.
Why it matters
Agents are useful when a task cannot be solved in one model call because each next step depends on previous results.
How it works
The app sends the task and tool definitions to the model. The model requests a structured tool call. Your code validates and runs the tool, sends the result back, and the loop continues.
Common mistakes
- Giving agents too many tools.
- Allowing high-impact actions without confirmation.
- Skipping traces and step limits.
Best practices
- Start with one or two tools.
- Log every action and observation.
- Set hard limits for steps, cost, and time.
Practical Workflow
goal -> choose tool -> run code -> observe result -> decide next step -> final answerFAQ
Are AI agents autonomous?
They can be partially autonomous, but production agents should have limits, validation, and human confirmation for risky actions.
Do agents always need memory?
No. Add memory only when the task needs continuity across steps or sessions.
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