For the past few years most of us have met AI as a chatbot: you type a question and get an answer. The direction the industry is now heading is the AI agent, which does not stop at answering. It takes a goal and works through several steps until the job is done.
How an agent differs from a chatbot
A chatbot answers one question at a time and does nothing afterwards. An agent breaks a goal into steps, calls the tools it has been allowed to use (searching, reading files, calling another system's API, writing and running code), and checks the result of each step before deciding what to do next.
The loop in four steps
- Take a goal, such as “summarise this month's complaints by category and draft a report for management”.
- Plan: split the work into smaller steps, such as fetch the data, group it, count it and write the summary.
- Use tools: carry out each step through connected systems such as databases, document stores or APIs.
- Check and repeat: if a result is wrong or incomplete, adjust the plan and try again until the work is ready to hand over.
Where organisations are using them
- Paperwork: read and summarise large volumes of reports, pull out the key figures and draft a first version of a letter or report.
- Answering from manuals and regulations: search the organisation's own documents and cite the source every time, so answers can be checked.
- Monitoring sensor data: watch for unusual readings, gather the data around an event and alert staff with a first summary.
- Software development: help write code and tests and track down bugs, with a developer reviewing before anything goes live.
What to think about first
- Least privilege: an agent should reach only the data and systems the task needs.
- A person approves key steps, such as emailing outside the organisation, changing live data or approving payments.
- Personal data: design for data-protection law and decide clearly which data may leave the organisation for processing.
- Keep a record of every step, so you can see what the agent did, when, and with which data.
- Measure on real work: test with real examples from your organisation before rolling it out further.
Start with a small, repetitive task whose results are easy to measure, then grow. That beats starting with one big system that tries to do everything.
Agents do not replace people. They take on repetitive, time-consuming work so teams can spend more time on decisions that need judgement. If you are looking for a place to start, the Kathi Studio team can help you work out which tasks suit AI and connect it safely to the systems you already run.