What is an AI agent? definition and examples
What an AI agent actually is, the loop it runs on, the four parts every agent has, where agents are reliable, and how they differ from automations.
Osman Ramadan10 min read
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"AI agent" has become one of those terms that means something slightly different every time you hear it. A chatbot with a plugin gets called an agent. So does a system that writes and deploys code on its own. The gap between those is enormous, and it's the reason conversations about agents so often produce more confusion than clarity.
This page is about the noun: what an AI agent actually is, what it's made of, and where it works. If you're trying to decide whether a piece of work wants an agent or something simpler, AI agents vs automation answers that directly. If you want the broader field, the design patterns and where the industry currently stands, see what is agentic AI.
What we'll cover
The definition
An AI agent is software that pursues a goal by choosing its own actions. You give it an objective rather than a procedure, and it works out the steps.
Stanford's AI Index Report describes AI agents as systems that can autonomously plan, use tools, and execute multi-step tasks toward user-defined objectives. The three parts of that are worth separating: planning means deciding the sequence, tools mean it can act rather than only answer, and multi-step means it continues based on what it learns along the way.
The useful contrast is with software that follows a procedure you wrote. A program that receives an invoice, extracts the total, and files it is executing your instructions. An agent given "keep our supplier records current" decides for itself what that requires today. The same underlying technology appears in both, but the relationship with the person who set it up is completely different.
The loop, which is the whole idea
Almost everything distinctive about agents comes from one structural feature: they run in a loop rather than in a line.
- Observe. The agent takes in the current state: a request, a trigger, or the result of what it just did.
- Decide. The model reasons about what to do next, given the goal and what it now knows.
- Act. It calls a tool, which is where it touches the world.
- Repeat. The result of that action becomes the next observation.
A single model call is a line: input, output, done. The loop is what lets an agent recover from a dead end, follow a thread it didn't anticipate, and keep going until the goal is met.
It's also where every difficulty comes from. Each pass through the loop is a decision you didn't make, a cost you didn't directly authorize, and an opportunity for the agent to head somewhere you didn't intend. Understanding agents really means understanding that the loop gives you the adaptability and the unpredictability together, and you can't take one without the other.
The four parts every agent has
A model. The reasoning engine that decides what happens next. Frontier models are all capable enough for most agent work, so this choice affects cost and speed more than whether the thing functions at all.
Tools. Functions the agent can call: search, read a record, send a message, run code. An agent without tools is a chatbot, because it can talk about the work but can't do any of it. Designing these well is most of the real effort in building one.
Memory. What the agent carries between steps, and sometimes between runs. Models are stateless, so any continuity is something the system around the model provides rather than something the model does.
A stopping condition. When the goal counts as met, and what happens when it doesn't. The least discussed and most consequential of the four, because an agent without a firm one will keep going, spending money and taking actions, long after a person would have stopped to ask.
Types of AI agent
Single-task agents. One clear capability: classify this message, extract these fields, summarize this document. Narrow scope, obvious success criteria, and the most reliable category by a wide margin. Most production AI work sits here, whatever it gets called in the marketing.
Multi-step reasoning agents. Chain several actions toward a goal, such as researching a topic across sources and producing a synthesis. The loop is doing real work here, and reliability drops as the chain lengthens, because each step inherits whatever the previous one got wrong.
Autonomous agents. Operate with minimal oversight across a broad domain: manage the inbox, handle support, watch the competition. The most ambitious category and the least dependable. Autonomy and reliability currently pull against each other, and anyone claiming otherwise is selling something.
Multi-agent systems. Several specialized agents collaborating, with one researching, one drafting, one reviewing. Each individual agent stays narrow and reliable while an orchestration layer coordinates them, which is a sensible design and also considerably more moving parts to get wrong.
Where agents are reliable and where they aren't
The single most important property of an agent is that it's probabilistic. The same input can produce different output. That's the nature of the thing rather than a defect to be engineered away, and whether it's acceptable depends entirely on the job.
An agent that works 95% of the time fails one run in twenty. For exploratory work that's fine, because a research summary that misses an angle costs you a read. For a process running four hundred times a year against customer records, one in twenty is a lot of wrong outcomes, and somebody has to notice each of them.
This is why so much production work described as agentic gets deliberately constrained until it stops being agentic. Teams add validation, confidence thresholds, fixed routing, and human review, and what emerges is a defined process with a model doing one specific job inside it. That's usually the right destination, and it's worth recognizing early rather than after six months of tightening.
Which is the practical distinction: an agent decides, an automation does what you described. Most business processes have a right answer, and for those you want the thing that produces it the same way every time. CodeWords is built for that work. You describe the process in plain language and Cody, the automation builder, builds it, connects it to the tools you already use, and deploys it, with more than 3,000 integrations available. The free plan covers light use, with Pro at $39 per month and Business at $100 per month; current details are on the pricing page.
What AI agents are actually used for
Three patterns cover most of what's genuinely running, rather than being demonstrated.
Research and enrichment. A record arrives with a company name attached. The agent finds the website, reads it, searches for recent news, pulls what's publicly known, and returns a structured summary. It suits an agent because the path varies: a well-documented company needs different steps from one with almost no web presence, and you can't specify in advance which you'll get.
Triage of unstructured input. Support messages, inbound applications, incident reports. The agent reads, categorizes, checks context in another system, and routes. The judgment is real, and the stakes are usually low enough that an occasional wrong routing is a minor cost rather than a serious one.
Investigation. Something looks wrong and the next question depends on the last answer. Why did this number move, where did this discrepancy come from, what changed. Genuinely open-ended, and the category where agents most clearly beat the alternatives.
What these share is that the sequence can't be written down in advance. Where it can, the same work is better served by something that runs it identically every time, which is the bulk of what most businesses want automated: the invoices, the reminders, the reports, the chasing. That work doesn't need an agent, and giving it one adds cost and variance without adding anything you wanted.
Frequently asked questions
Is ChatGPT an AI agent?
Not in its basic form, which is a conversation: you ask, it answers, and nothing happens in the world. With tools, code execution, and browsing it takes on agent characteristics, because it can plan and act. The distinction most people care about is whether it runs without someone sitting there, and a chat interface by definition doesn't.
What's the difference between an AI agent and a chatbot?
A chatbot's output is the conversation. An agent's output is an action. A chatbot can tell you your order shipped; an agent can look up the order, notice it didn't ship, and raise a ticket about it. Many products are both, with a conversational front end over something that acts.
What's the difference between an AI agent and an automation?
Who chooses the steps. An automation runs the process you described, the same way every time. An agent works out its own route to the goal. Neither is more advanced, and they suit different work, which is set out in AI agents vs automation.
Do AI agents learn from experience?
Usually not in the way the phrase suggests. Most agents don't update the underlying model based on what happens; they adapt within a single run based on what they observe. Anything resembling learning across runs is typically a memory system somebody built, storing past outcomes and feeding them back in as context.
Are AI agents ready for production?
Narrow ones, yes, with guard rails. Single-task and short multi-step agents run in production at plenty of companies. Broadly autonomous agents remain experimental for most uses, and the honest pattern is to start narrow, measure how often it's right, and widen only when the numbers justify it.
How much do AI agents cost to run?
More variably than people expect, because cost scales with loop iterations rather than with requests. A task taking four model calls on clean input can take twenty on messy input, so the bill tracks your data quality as much as your volume. Test against real inputs rather than tidy examples before committing to a budget.
Can I build an AI agent without being a developer?
Building a genuine agent involves tool definitions, error handling, and debugging a loop, which are programming tasks whatever the marketing says. Building an automation, which is what most people actually want, doesn't: you describe the process in plain language. Worth being clear which of the two your problem needs before choosing how to build it.
What's the difference between an AI agent and a workflow?
A workflow is the route through a process, laid out in advance. An agent works out its own route. The confusion comes from products that call a workflow an agent because a model appears somewhere inside it, which is now common enough that the word alone tells you very little about what you're looking at.
Do AI agents need supervision?
The consequential ones do. The reasonable default is that anything irreversible, anything touching money, and anything a customer sees has a person in the path. Supervision isn't a sign the agent is immature, it's the design that makes autonomy affordable, because reviewing prepared work is much faster than doing it.
How do I know if something is really an agent?
Ask what happens on an input nobody anticipated. A defined process either handles it by a rule you wrote or stops. An agent improvises. That single behavioral difference is more reliable than any description on a product page, and you can usually establish it in a demo by asking the vendor to show you an awkward case rather than the prepared one.
Related reading
- AI agents vs automation: what's the difference?
- What is agentic AI?
- Build your own AI agent
- CodeWords integrations and templates
The word covers a lot of ground, and the part that matters is always the same question: does this thing decide, or does it do what you told it? Both answers are respectable, and knowing which one you have is what keeps the surprises to a minimum.