For the past few years, most business uses of generative AI have looked like a conversation: a person asks a question, a model answers. AI agents change that pattern. Instead of responding once, an agent breaks a goal into steps, calls tools and APIs, checks its own progress and keeps going until the task is done — or until it needs a human.

That shift from answering to acting is why agents are attracting so much attention. It is also why they demand more care than a chatbot.

What makes something an "agent"?

The term is used loosely, so it helps to be precise. In practice, an AI agent combines four ingredients:

  1. A goal, expressed in natural language or structured input ("reconcile last week's supplier invoices").
  2. A reasoning model that plans the next step based on the goal and what has happened so far.
  3. Tools — APIs, databases, search, email, internal systems — that the agent is allowed to call.
  4. Memory and state, so it can track progress across many steps.

Remove the tools and you have a chatbot. Remove the reasoning and you have a traditional automation. Agents sit in between: flexible enough to handle variation, structured enough to take real actions.

Where agents create value today

The strongest early use cases share three traits: the work is high-volume, it follows a recognisable pattern with frequent exceptions, and the cost of a mistake is recoverable. Examples we see delivering results:

  • Accounts payable exceptions. Investigating mismatched invoices, pulling contracts and purchase orders, and drafting supplier queries.
  • IT service desk triage. Classifying tickets, gathering diagnostics, applying known fixes and escalating with a complete summary.
  • Sales operations. Researching accounts, updating CRM records after calls and preparing renewal briefs.
  • Customer support. Resolving order, delivery and billing questions end to end, with handover to a human for refunds above a threshold.

Notice what is not on the list: decisions that are irreversible, heavily regulated or reputationally sensitive. Those can involve agents, but only as assistants to a person who makes the final call.

Designing for safety from day one

An agent is only as safe as the permissions you give it. We recommend five design principles for any production deployment:

1. Least-privilege tools

Give each agent a narrow set of tools with scoped credentials. An agent that drafts supplier emails does not need permission to send payments.

2. Human-in-the-loop checkpoints

Define explicit approval steps for actions above a value, risk or confidence threshold. The agent prepares; the human approves.

3. Deterministic guardrails

Wrap the model in ordinary code: input validation, output schemas, rate limits and business rules that the model cannot override.

4. Full observability

Log every plan, tool call and result. When something goes wrong — and it will — you need to replay exactly what the agent saw and did.

5. Continuous evaluation

Build a test set of real scenarios and run it before every change to prompts, models or tools. Measure task success, cost per task and escalation rate.

The question is not "can an agent do this task?" It is "can we prove the agent does this task reliably enough, at a cost that makes sense?"

Measuring whether it is working

Agent projects fail most often because nobody defined success. Before you start, baseline the current process and agree on a small set of metrics:

Metric What it tells you
Task completion rate How often the agent finishes without human help
Escalation quality Whether hand-offs arrive with everything a person needs
Cost per task Model, infrastructure and review time combined
Cycle time How long the end-to-end process now takes
Error rate Mistakes caught in review or downstream

A good first target is not full autonomy. It is an agent that handles the routine 60–70% of cases and hands the rest to people with a clear, accurate summary.

A 90-day path to production

  1. Weeks 1–2: Select and baseline. Pick one process, measure it and define success.
  2. Weeks 3–6: Build a supervised pilot. Narrow tools, human approval on every action, full logging.
  3. Weeks 7–10: Evaluate and harden. Expand the test set, tune guardrails, reduce approvals where the data supports it.
  4. Weeks 11–13: Scale carefully. Roll out to more users or volume, with monitoring and a clear rollback plan.

The bottom line

AI agents are not a replacement for good process design — they amplify it. Organisations that start with a well-understood workflow, strong guardrails and honest measurement are already seeing meaningful gains. Those that start with the technology and look for a problem are not.

If you are exploring where agents could fit in your business, our AI & Machine Learning team runs a two-week discovery sprint that ends with a working, evaluated prototype on your own data.