AI Agent definition
An AI agent is a software system that uses a large language model to pursue a goal by deciding which steps to take, calling tools such as APIs, databases or a browser, observing the results and continuing until the task is done. Unlike a chatbot that only replies, an agent takes actions within limits set by its developers.
How does an AI agent work?
An agent runs a loop. It receives a goal, reasons about the next step, calls a tool, reads the result and decides what to do next, repeating until it finishes or hits a limit. This pattern, often called ReAct for reasoning and acting, turns a language model from a text generator into something that can look up an order, query a database, update a ticket or run code.
- Model: the LLM that reasons and chooses actions.
- Instructions: the role, rules and boundaries the agent must follow.
- Tools: functions and APIs the agent may call, each with a defined schema.
- Memory: conversation state plus any stored facts from earlier sessions.
- Orchestration: the code that runs the loop, enforces limits and handles errors.
Types of AI agents
Classic AI textbooks group agents as simple reflex, model-based, goal-based, utility-based and learning agents, by how they choose actions. Today the term usually means LLM-powered agents, which come in a few practical forms: task agents with a small toolset, such as a support agent; coding agents that read and edit repositories; computer-use agents that operate a browser or desktop; and multi-agent systems where specialized agents hand work to one another. Most production agents today are narrow task agents.
AI agent vs chatbot vs workflow automation
A chatbot answers in conversation but does not act. A workflow automation, built in a tool such as Zapier, n8n or a BPM platform, follows fixed steps defined in advance. An agent sits between them: it decides the steps at run time based on the situation. That flexibility handles messy, varied requests, but it is less predictable, so the best systems use fixed workflows wherever steps are known and agents only where judgment is needed.
Example: an order support agent
A customer writes that a parcel arrived damaged. The agent looks up the order, checks the delivery status and photos, reads the refund policy and confirms the item is eligible. Below a set amount it issues a refund through the payments API and emails a confirmation. Above that amount, or if anything looks unusual, it prepares a summary and hands the case to a human agent for approval. Every step is logged so the team can review decisions later.
How to build reliable AI agents
Errors compound across steps, so a model that is right most of the time per step can still fail long tasks. Agents also face prompt injection, where text in an email or web page tries to hijack their instructions. Keep tasks narrow, give each tool the least privilege it needs, require human approval for irreversible actions, set step and spending limits, and log every tool call for audit.
Test agents against a suite of realistic scenarios, including adversarial ones, before release and after each change. Nexzem builds AI agents with these controls from the first prototype, starting with read-only tools and adding write actions only once evaluation results support it.