What is an AI agent

Introduction to AI agents

An AI agent works toward a goal you set, deciding its own next step at each stage instead of waiting for you to direct it. Where a standard program runs a fixed sequence and a chatbot replies to one prompt at a time, an agent breaks a goal into steps and uses tools to carry them out.

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Most agents today are built on a large language model (LLM), which supplies the reasoning, paired with access to external tools such as databases, APIs, web searches, or other software. The language model decides what needs to happen and the tools let the agent do it. This is why agents are sometimes called LLM agents, and it is what lets them act on current, specific data rather than only on what the underlying model was trained on.

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The practical test is simple: if a system waits for you to tell it what to do next, it is a tool; if it decides for itself based on what just happened, it is an agent.

How an AI agent works

Most agents operate in a loop that repeats until the goal is reached. The same cycle underpins systems of very different complexity.

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Set a goal and plan

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The agent receives a goal and the tools it is allowed to use, both defined by a person. For anything beyond a simple task, it breaks the goal into smaller steps, a process known as task decomposition. Simple tasks may need no planning at all; the agent can act and then refine.

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Reason and act

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The agent works through its plan, calling tools when it lacks the information or ability to complete a step on its own. It might query a database, call an API, run code, or pass part of the work to another agent. Each tool result updates what the agent knows.

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Observe and adapt

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The agent takes in the result of each action and feeds it back into the next decision. If something has changed or a step has failed, it can adjust the plan rather than carry on regardless.

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Many agents also keep memory across steps and sessions, so they can hold context as a task unfolds and draw on past interactions to inform later ones.

AI agents and human oversight

Autonomy does not mean an agent runs unchecked. Because an agent takes real actions, a person usually stays in the decision path for anything consequential, an arrangement known as human-in-the-loop AI: the agent recommends or drafts an action, and a person approves, edits, or rejects it before it takes effect. This matters most where a wrong action is costly or hard to reverse, such as moving money or sending communications at scale.

AI agents in finance

In finance, agents are applied to work that is repetitive, data-heavy, and run on a schedule, from assembling a daily cash position to reviewing payments and preparing recurring reports. How much they help depends on the data underneath: an agent working from fragmented or out-of-date figures produces fragmented or out-of-date answers, which is why reliable, real-time access to financial data is the precondition for useful agents in this setting. For more on this, see what are AI agents in treasury? and what is AI in treasury management?.

How Atlar uses AI agents

Atlar runs AI agents on your live treasury data. The connections to your banks and ERP are built and managed by Atlar, so the agents work from current balances and transactions rather than exported or stale figures. It is the approach that led AI-first companies like Lovable and Trustly to choose Atlar.

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The agents take on recurring operational work and present their output for you to review, across cash positioning, payments, reconciliation, and forecasting. Access is governed by your existing user permissions and role-based controls, so nothing happens until your team approves it, and you can reach your data from inside Claude. 

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Atlar is the top-rated treasury platform on G2, rated 4.8 on average across 60+ reviews. To see how Atlar's agents work, see Atlar Intelligence, or book a demo with our team.