How an AI agent works
At the centre is usually a large language model. Given a goal such as “reorder the parts that are running low”, it plans a first step, calls a tool (a database query, an email, a ticket update), reads what came back, and decides the next step. It repeats this loop until the goal is met or it needs a person to decide.
Around the model sit the parts that make it dependable: a list of the tools it may use, a memory of what it has already done, rules about what it must never do, and a log of every action so that people can check the work afterwards.
Where agents are used
Agents suit work that is routine, spread across several programs, and easy to check: sorting support requests, scheduling, drafting reports from several sources, matching invoices to orders, or helping developers change code. They do best where a mistake is cheap to catch and undo.
Limits and risks
Agents can misread an instruction, act on out-of-date data, or get stuck in a loop. A document or web page can also contain hidden text written to steer an agent, an attack known as prompt injection. For these reasons agents are usually given narrow permissions, and a person approves anything with real consequences, such as spending money or deleting records.
Where it is used
- Customer support Sorting and routing requests and drafting replies for a person to approve.
- Operations Matching invoices to orders, flagging mismatches and filing routine tickets.
- Software teams Reading a codebase, proposing changes and running tests for a developer to review.
AI agents compared with chatbots and fixed workflows
A chatbot answers one message at a time. Traditional workflow automation follows rules written in advance and breaks when something unexpected appears. An agent sits between them: it decides its own next step, inside the tools and limits it has been given.
That flexibility is its strength and its risk. A fixed workflow always does the same thing; an agent can surprise you. For steps that must be exactly right every time, a fixed workflow is still the better choice.
Key terms
- Agent
- Software that works toward a goal in steps, using other software as tools.
- Tool call
- A request from the model for another program to do something, such as search or send a message.
- Prompt injection
- Hidden or malicious text in content that tries to steer what a model does.
- Human in the loop
- A design in which a person approves the important actions.
Common questions
Is an AI agent the same as a chatbot?
- No. A chatbot replies to what you type. An agent can also take actions in other software to reach a goal, and keeps going across several steps.
Will AI agents replace people?
- Today they mostly take over small, repetitive tasks and leave judgement to people. How far that goes depends on the job, the quality of the tools and the rules a business sets.
How do you keep an agent safe?
- Give it the least access it needs, keep a full log, test it on realistic cases first, and require human approval for actions that cannot easily be undone.
Sources and further reading
Independent pages we checked while writing this guide. They are not Reinnder products, and Reinnder is not affiliated with them.
Reinnder’s angle
How Reinnder looks at aI agents at work
Reinnder AI is planned around agents that work on a customer’s own data, with a person approving the decisions that matter.
This guide explains the technology in general terms. It is not advice, and it does not describe a Reinnder product on sale.