What is AI Agents?
An AI agent is a system that uses a large language model to pursue a goal by reasoning, choosing and calling tools (like APIs, databases, or search), and acting in multiple steps — rather than just answering a single prompt.
At a glance
- Goes beyond a single answer: plans, calls tools, and acts over multiple steps.
- Built from a few patterns: tool use, retrieval (RAG), routing, reflection, planning.
- Uses tools (APIs, databases, search) to take real actions, not just chat.
- Complex agents coordinate specialized sub-agents (multi-agent systems).
How an agent is different from a chatbot
A chatbot responds to one prompt with one answer. An agent is given a goal and figures out how to reach it: it reasons about what's needed, decides which tool to call (query a database, hit an API, search the web), reads the result, and repeats until the task is done. The model does the thinking; the tools let it act on the real world.
That loop — think, act, observe, repeat — is what makes agents useful for real work like triaging a support ticket, processing an invoice, or verifying a loan application end to end.
The patterns underneath
Nearly every production agent is assembled from a small set of patterns: Tool Use (call systems and extract parameters), RAG (retrieve grounded facts before answering), Routing (choose between paths), Reflection (check its own work), and Planning (break a goal into steps). Harder problems combine several, and Multi-agent designs let specialized agents collaborate.
Choosing the right patterns for a task — and knowing where the reliable boundaries are — is the core skill in building agents that work in production rather than in a demo.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot answers a single prompt. An AI agent pursues a goal over multiple steps — reasoning, calling tools to take real actions (APIs, databases, search), reading the results, and iterating until the task is complete.
What is 'tool use' in an AI agent?
Tool use is the pattern where the model decides which external tool to call and fills in the parameters — for example querying a database or hitting a payment API. It's what lets an agent act, not just talk, and it's the most common agent pattern.
What is a multi-agent system?
A multi-agent system uses several specialized agents that collaborate, usually coordinated by a lead agent — for example one agent per stage of a hiring or claims workflow. It's how complex, multi-step goals get decomposed.
Are AI agents reliable enough for production?
They can be, when scoped well and given verification patterns like reflection and grounded retrieval (RAG). The key is matching the agent's complexity to the task and adding checks where mistakes are costly.
Putting AI Agents to work?
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