What is an AI agent and how does it work?
Core components
An AI agent typically combines a large language model (LLM) as the reasoning engine with tools, memory, and a planning mechanism. The LLM interprets the user's request and decides what to do next.
Tools let the agent interact with the outside world—searching the web, querying databases, sending emails, or calling APIs. Memory stores context from previous steps so the agent doesn't repeat itself.
A planner breaks a high-level goal into smaller steps and decides the order to execute them. Some agents use simple prompting, while others use more structured frameworks like ReAct or chain-of-thought.
- Perception: reading input from users, APIs, or sensors
- Reasoning: using an LLM to decide the next action
- Action: calling a tool or generating a response
- Memory: short-term (conversation) and long-term (vector databases)
- Loop: repeating until the goal is met or a stop condition is reached
How the loop works
The agent starts with a goal, such as 'book me a flight to Chicago.' It observes the current state (no flight booked), thinks about what to do (search for flights), and acts (calls a flight search API).
After each action, the agent observes the result and updates its plan. This cycle continues until the agent decides the goal is achieved or it hits a limit like a maximum number of steps.
Some agents are reactive (respond to immediate input) while others are proactive (initiate actions based on goals or schedules). Most modern agents are built on top of LLMs because they can handle open-ended language tasks.
Common mistakes
- Thinking an AI agent is just a chatbot—chatbots respond, but agents also take actions and pursue goals.
- Assuming the agent always works perfectly; it can get stuck in loops or make mistakes, so guardrails are important.
- Believing you need a custom model; most agents use existing LLMs via API.
