An AI agent is an AI system that pursues a goal by taking actions, rather than answering a single question and stopping. Given an objective, it plans steps, uses tools, observes what happened, and decides what to do next, looping until the job is done or it gives up.
The difference from a chatbot is the difference between advice and action. A chatbot tells you how to book a flight. An agent opens the site, searches, and fills the form.
The loop that makes it an agent
Almost every agent runs some version of the same cycle:
- Understand the goal it was given
- Plan the next step
- Act, by calling a tool: a web search, a calculator, a database query, a code interpreter, an API
- Observe the result
- Decide whether the goal is met, and if not, return to step 2
The large language model provides the reasoning at each step. The tools provide everything the model cannot do itself, which is a longer list than people assume: it cannot browse, cannot calculate reliably, cannot remember across sessions, and cannot affect anything in the world without being connected to something.
A worked example
Ask an agent to research the best current fixed deposit rates for a senior citizen and produce a comparison.
A chatbot answers from training data, which may be two years out of date. An agent would search for current rates, open several bank pages, extract the relevant tenures and senior citizen premiums, notice that one page failed to load and retry, compile the figures into a table, and hand you a document with sources.
Same underlying model. The difference is the ability to act, observe and iterate.
What agents are actually good at today
- Multi-step research where the sub-questions are not known upfront
- Writing code, running it, reading the error, and fixing it
- Extracting structured data from many documents
- Routine multi-app workflows: read a form submission, look up a record, draft a reply
- Monitoring something and acting when a condition is met
Where they fall down
Errors compound. A single wrong step early can send the whole run in the wrong direction, and the agent will pursue it with the same confidence it applies to correct steps.
Loops and stalling. Agents get stuck repeating an action that is not working, or declare success without having achieved anything.
Cost. Every step consumes tokens, and a long run can consume a very large number of them.
[Hallucination](/glossary/hallucination) does not disappear. An agent can invent a fact, act on it, and then reason from the consequence as though it were established.
Judgement is absent. An agent does not know that a step is unusual, irreversible or a bad idea unless you told it.
Guardrails, which are the whole practical question
Because an agent acts rather than suggests, the important design decisions are about limits, not capability.
- Restrict what it can touch. Read-only access wherever possible. An agent that can query a database is far safer than one that can modify it.
- Require confirmation for anything irreversible. Sending messages, spending money, deleting records, publishing anything. A human should approve each of these.
- Cap the run. Maximum steps, maximum spend, a hard timeout.
- Log everything. You need to be able to reconstruct what it did and why.
- Sandbox it. Give it a scratch environment rather than production access.
- Never hand it credentials it does not need. Financial account access in particular should not be delegated to an autonomous process.
Practical notes for use in India
Never let an agent transact. Do not give an agent access to net banking, UPI, a trading account or a payment method. Even setting aside failure modes, an autonomous process moving money is a category of risk with no upside proportionate to it.
Watch what it sends. An agent with email or messaging access can send something on your behalf that you would not have sent. Approval before sending is the minimum sensible setting.
Data protection applies. An agent reading customer records and pushing them into a third-party AI service raises real obligations under the Digital Personal Data Protection Act. This is an organisational compliance question, not just a technical one.
Verify factual output. An agent that searched and cited sources is more reliable than one working from memory, but the summarising step can still distort. Check the sources it named.
Where this is heading
Agents are improving quickly and are already genuinely useful for coding, research and structured data work. They remain unreliable enough that unsupervised operation on anything consequential is not yet reasonable.
The sensible current posture is to use them where the work is easy to verify and the cost of an error is low, and to keep a human approving anything that touches money, sends a message, or cannot be undone. They build on prompt engineering for steering and on generative AI for the underlying capability. Explore practical tools in our AI tools directory.