What Are AI Agents? A Plain-English Guide

The term "AI agent" is suddenly everywhere, but it means something specific, and different from the chatbots most people have used. A chatbot answers a question. An AI agent takes a goal and goes and accomplishes it: it figures out the steps, pulls the data it needs, uses tools, takes action, checks the result, and keeps going until the job is done. That shift, from answering to doing, is what all the attention is about.

This is a plain-English guide to what AI agents are: how they work, the main types, what they can and cannot do, real examples across business, and where they are heading. No hype, no jargon you have to already know.

What is an AI agent?

An AI agent is a software system that pursues a goal autonomously, perceiving its situation, reasoning about what to do, planning a sequence of steps, using tools and data, and taking actions, with limited human intervention. Instead of producing a single response to a single prompt, it works toward an objective over multiple steps and adapts as it goes.

The simplest way to define an AI agent: give it a goal, not just a prompt, and it will plan and act to achieve it. A generative AI model writes you an email if you ask; an AI agent can decide an email is needed, draft it, and send it as part of a larger task.

Most modern AI agents are built on a large language model, the same kind of model behind ChatGPT or Claude, as their reasoning engine. What turns that model into an agent is the machinery around it: the ability to plan, remember, call tools, and act in the world.

How do AI agents work?

Under the hood, an AI agent runs a loop. The pieces:

• Perception / input. It takes in a goal and relevant context, a request, data from your systems, the result of a previous step.
• Reasoning and planning. Using its language-model core, it breaks the goal into steps and decides what to do next.
• Tools and actions. It calls tools, querying a database, hitting an API, running code, sending a message, to actually do things, not just describe them.
• Memory. It keeps track of what it has done and learned across steps (and sometimes across sessions), so it can build on prior work.
• Observation and iteration. It observes the result of each action, checks progress against the goal, and adjusts, looping until the task is complete or it needs a human.

That loop, plan, act, observe, adjust, is what separates an agent from a one-shot model. It is also why agents can handle messy, multi-step work that a single prompt cannot.

AI agents vs. chatbots and generative AI

These get lumped together, but they are different layers:

Agents are typically built on generative models, so it is not either/or; the agent uses the model to think, then adds planning and action. We break this down further in our guide to agentic AI vs. generative AI.

Types of AI agents

You will see AI agents categorized a few ways. Two useful lenses:

By capability

• Simple reflex agents react to the current input with fixed rules, no memory of the past.
• Goal-based agents plan a sequence of actions to reach a defined objective.
• Learning agents improve over time from feedback and outcomes.
• Tool-using / LLM agents (the current wave) use a language model to reason and a set of tools to act, the type behind most business AI agents today.

By structure

• Single-agent systems have one agent handling a task end to end.
• Multi-agent systems coordinate several specialized agents, one to research, one to write, one to review, working together on a larger goal.

What can AI agents do? Real examples

Agents are showing up across business functions wherever work is multi-step and rules-plus-judgment:

• Software engineering. Agents that read a codebase, write code, run tests, and open pull requests.
• Customer support. Agents that resolve a ticket end to end, looking up the account, taking the action, and replying.
• Research and analysis. Agents that gather sources, synthesize, and produce a report.
• Operations. Agents that move data between systems, reconcile records, and flag exceptions.
• Finance. Agents that run the close, reconcile accounts, build forecasts, and produce reporting, with a human approving, which is what we focus on at Concourse.

Benefits and limitations

The upside is real, and so are the constraints. Be clear-eyed about both.

Benefits

• They do the work, not just draft it, taking whole tasks off people's plates.
• They handle multi-step, messy processes that a single prompt cannot.
• They work across your existing systems through tools and integrations.
• They free people for judgment, moving human effort from execution to review and decisions.

Limitations

• They can make mistakes. Because they are probabilistic, output needs review, especially where it must be correct.
• They need guardrails. Autonomy over real actions raises the stakes, so trusted deployments keep a human approving consequential steps.
• They depend on good data and tools. An agent is only as good as the systems and context it can reach.
• Trust and traceability matter. In regulated or high-stakes work, every output has to be defensible, which is why traceability back to source is essential.

AI agents in finance

Finance is one of the clearest places agents earn their keep, because so much of the work is high-frequency, multi-step, and rules-plus-judgment: closing the books, reconciling accounts, forecasting, and reporting. The catch is that finance output has to be defensible to a board or an auditor, so the durable model is agents that do the work with a human approving and every number traceable back to source.

That is exactly how Concourse applies agents: connecting to your ERP, data warehouse, and banking data to run finance workflows end to end, with traceability built in. For the finance-specific view, see what AI agents do for finance teams and our guide to AI agents for finance automation.

Frequently asked questions

What is an AI agent?

An AI agent is a software system that pursues a goal autonomously, reasoning, planning, using tools, and taking action over multiple steps, rather than just answering a single prompt. Most are built on a large language model as the reasoning engine, with added planning, memory, and the ability to act.

How do AI agents work?

An AI agent runs a loop: it takes a goal, plans the steps, uses tools to act (querying data, calling APIs, taking actions), observes the result, and adjusts, repeating until the task is done or it needs a human. The language-model core does the reasoning; the surrounding tools and memory let it act and improve.

What is the difference between an AI agent and a chatbot?

A chatbot answers questions or generates content one turn at a time and does not take action on its own. An AI agent pursues a goal across multiple steps, using tools and data to actually complete work. Agents are typically built on the same models that power chatbots, with planning and action added.

Are AI agents safe to use for important work?

They can be, with the right guardrails. Because agents are probabilistic and take real actions, trusted deployments keep a human approving consequential steps and require every output to be traceable back to source. That is especially important in regulated or high-stakes work like finance.

The bottom line

AI agents are the shift from software that answers to software that does: systems that take a goal, plan, use tools, and act to get work done, with a human in the loop where it counts. They are built on generative models but go further, and they are already handling multi-step work across engineering, support, operations, and finance.

If you want to see what AI agents look like applied to real finance work, run end to end with every number traceable to source, talk to our team and put an agent on a live workflow.