Definition
An AI agent wraps an LLM in a loop: observe state, decide the next action, call tools (search, APIs, code execution), and repeat until a goal is met.
Agents extend chatbots into systems that can browse, write files, query databases, and orchestrate multi-step workflows.
In simple terms
A chatbot answers questions. An agent is like a junior employee with a laptop — you give a goal, and they look things up, send emails, run calculations, and report back when done.
Where you see it
- Coding agents that edit repositories and run tests.
- Research agents that search papers and synthesize summaries.
- Customer ops agents that look up orders and trigger refunds via APIs.
How it works
1.Goal and tools
Define the objective and register tools the agent may call (with schemas).
2.Reasoning loop
The LLM plans subtasks and chooses tool invocations based on observations.
3.Execute and observe
Tools run; results feed back into context for the next iteration.
4.Stop condition
The agent finishes when the goal is satisfied or limits (steps, cost) are hit.
Why it matters
- Agents automate multi-step knowledge work beyond single-shot prompts.
- They combine LLMs, RAG, and software engineering into product features.
Often confused
Agents are fully autonomous and safe to run unsupervised.
They need guardrails, permission scopes, and human oversight — tool misuse and loops are real risks.