"AI agent" has become one of the most stretched terms in software. Some products use it for a chatbot, others for a fully autonomous system. For a business deciding where to invest, a precise definition matters, because it determines what can go wrong and how much oversight you need.
What an AI agent actually is
An AI agent is a language model that can decide which actions to take to reach a goal. It is given instructions, a set of tools (such as "search the CRM", "create a ticket" or "send an email") and some context. It then works in a loop: reason about the next step, call a tool, look at the result and decide what to do next, until the task is done or it hands over to a person.
That loop is what separates an agent from two things it is often confused with:
- A chatbot answers questions in a conversation. It may use a model, but it does not necessarily take actions in your systems.
- An automated workflow follows a fixed sequence of steps defined in advance. It can include AI steps, for example classifying an email, but the path through the process is fixed by the person who designed it.
Workflows first, agents where paths vary
Most business processes are more predictable than they first appear. When the steps are known, a workflow with one or two AI steps is usually cheaper, faster and easier to test than an agent. A support ticket can be classified by a model and then routed by ordinary rules.
Agents earn their place when the path genuinely varies from case to case. Researching a new lead across several sources, investigating why an order is delayed, or answering an internal question that requires looking in three systems are good examples. The model needs to choose what to look at next based on what it has found.
A useful rule: start with a workflow, and introduce agent behaviour only in the steps where a fixed sequence breaks down.
Where agents help today
The strongest use cases share three traits: the task is repetitive, the inputs are messy or unstructured, and a mistake can be caught before it causes harm.
- Lead qualification and enrichment. Research an inbound lead, summarise fit and intent, and prepare the CRM record for a salesperson to review.
- Support triage. Classify incoming tickets, pull relevant account context and draft a reply for an agent to approve.
- Document processing. Extract fields from invoices, contracts or forms, validate them against other systems, and flag exceptions.
- Internal knowledge questions. Answer policy or product questions using your own documentation, with links to the sources used.
- Operational reporting. Gather metrics from several tools and write a plain-language summary of what changed and why it might matter.
Where to be careful
Agents are a poor fit, at least without human review, for actions that are irreversible, high-stakes or poorly defined. Issuing refunds, changing prices, sending contractual commitments or deleting data should involve an approval step. So should any process where nobody can clearly describe what "correct" looks like, because you cannot evaluate an agent against a standard that does not exist.
Designing agents for reliability
Language models can be wrong, and they can be confidently wrong. Reliable agents are designed around that fact:
- Constrain the tools. Give the agent only the actions it needs, with the narrowest permissions possible. Read-only access is the safest default.
- Use structured outputs. Ask the model for data in a defined schema, then validate it before anything is written to your systems.
- Set thresholds and hand-offs. Route low-confidence or unusual cases to a person instead of forcing an answer.
- Keep humans in the loop for sensitive actions. An approval step in Slack or email is cheap insurance.
- Log every run. Record inputs, tool calls, outputs and costs so issues can be traced and fixed.
- Evaluate before and after launch. Build a set of real examples with known correct answers and re-test whenever prompts, models or tools change.
- Cap cost and time. Limit how many steps and how much spend a single run can use.
A rollout plan that works
- Pick one process with a measurable baseline, such as time to first response or hours spent per week.
- Map the process and define success, including the cases that should always go to a person.
- Build with human approval on, so the team reviews every action while trust is established.
- Measure against the baseline for a few weeks, and review errors in detail.
- Expand autonomy gradually, removing approval only for the cases where the agent has proven reliable.
This approach is slower than switching on a fully autonomous agent, but it produces something your team trusts and actually uses.
Getting started
If you have a process in mind, our AI & Automation service covers the full path from process mapping to monitored production agents. If you are still deciding where AI fits, a short consulting engagement can rank the opportunities first.