An AI chatbot answers questions. An AI agent goes further: it can plan a sequence of steps and use tools — search your knowledge base, look up an order, draft an email, update a record — to complete a task. That capability is genuinely useful, and also easy to over-apply. This guide is about choosing well.
What an AI agent actually is
Most business agents combine four parts:
- A language model that interprets the request and decides what to do next.
- Tools — functions the agent is allowed to call, such as “search policies”, “get order status”, or “create support ticket”.
- Knowledge — your approved content, retrieved at the moment it is needed (often called retrieval-augmented generation, or RAG).
- Guardrails — rules on what the agent may do on its own, what needs human approval, and what it must refuse.
Where agents work well
- High-volume, well-documented questions. Customer support, internal IT and HR help desks, and product questions where the answer exists in your documentation.
- Multi-step lookups. “Where is my order and can I change the delivery address?” requires checking several systems — tedious for people, straightforward for an agent with the right tools.
- Drafting with review. First drafts of replies, summaries of long threads, or proposals that a person checks before sending.
- Triage and routing. Classifying incoming requests and sending them to the right team with the context attached.
Where they struggle
- Undocumented knowledge. If the answer lives only in someone’s head, the agent cannot find it. Fix the documentation first.
- High-stakes decisions without review. Credit approvals, medical advice, and legal positions need a qualified person in the loop.
- Processes that change weekly. Agents follow the tools and content they are given; constantly shifting rules lead to inconsistent behaviour.
- Problems a simple form or rule would solve. If a dropdown and an automated email would do the job, they will be cheaper and more predictable.
How to pick a first use case
Score candidate use cases on four questions:
- Volume: does this task happen often enough to matter?
- Documentation: is the knowledge needed written down and current?
- Risk: what is the worst outcome of a wrong answer, and can a human catch it?
- Measurability: can you measure success — resolution rate, time saved, accuracy?
The best first projects score high on volume, documentation, and measurability, and low on risk. Internal help desks are often a good starting point because mistakes are visible and recoverable.
Designing for reliability
- Ground every answer in approved sources and show citations so users and reviewers can check them.
- Limit tools to what the task needs. An agent that can only read order status cannot accidentally refund one.
- Require approval for actions with consequences — sending messages externally, changing records, spending money.
- Build an evaluation set of real questions with known good answers, and run it before every change.
- Log conversations and collect feedback so quality can be monitored and improved.
- Make hand-off to a human easy and pass along the full context.
Data privacy
Decide early what data the agent may see, where it is processed, and how long it is retained. Most major model providers offer business terms that exclude your data from training, and some workloads can run on private or region-specific deployments. Treat customer data in an agent with the same care as in any other system.
A sensible rollout
- Start with an internal pilot for a small group of staff.
- Measure against your evaluation set and real usage for a few weeks.
- Fix gaps in documentation the pilot exposes — there are always some.
- Expand to customers with clear labelling and an easy route to a person.
- Add actions (not just answers) once accuracy is proven.
If you would like help identifying and building a first agent, see our Generative AI & AI Agents service, or start with an AI consulting review of where AI would genuinely help your business.