Industry Guide Software & AI
An AI agent decides its own next step and uses tools to reach a goal. That makes it powerful for some tasks and expensive or risky for others. Here is how to tell the difference.
An AI agent is software that uses a language model to work toward a goal by deciding its own next steps: it reads the situation, chooses a tool such as a search, a database query or an API call, looks at the result and decides what to do next, repeating until the task is done or it hands over to a person. That ability to plan and act is what separates an agent from a chatbot that only answers questions, and from workflow automation that follows a fixed path.
For businesses, the practical question is not whether agents are impressive. It is which tasks genuinely benefit from software that decides its own steps, and which are better served by simpler, cheaper and more predictable automation. This guide explains what AI agents are, where they are useful today, what they cost and how to start without taking on unnecessary risk.
Most business agents combine four parts:
Anthropic's engineering guidance on building effective agents draws a useful distinction: workflows follow predefined code paths, while agents let the model direct its own process and tool use. Many successful business systems combine the two.
| Type | What it does | Decides its own steps? | Best for |
|---|---|---|---|
| Chatbot | Answers questions in a conversation | No | FAQs and simple information |
| AI assistant | Helps a person draft, summarize or analyze | No, the person directs it | Individual productivity |
| Workflow automation | Moves work through fixed steps | No | Repeatable, rule-based processes |
| AI-assisted workflow | Fixed steps with a model at specific points | No | Messy inputs with predictable handling |
| AI agent | Plans and acts with tools toward a goal | Yes, within limits | Varied tasks where the path differs case by case |
Our guide to AI agents vs workflow automation explains how to choose between these for a specific process.
These are generic illustrations of where agents are being applied, not case studies.
The common pattern is that the agent prepares, investigates or completes bounded work, while people approve anything consequential.
Agent costs come from more than the model:
In the worked example in our guide to what AI applications cost to run, moving a question-answering feature to a five-call agent design increased model cost about eightfold before retries. That can be worth it if the agent resolves substantially more tasks, but it needs to be proven with measurement.
The following figures are invented for illustration.
An IT service desk receives 1,200 tickets a month. An agent gathers device details, checks known issues and drafts a first diagnosis before a technician opens each ticket. If that saves three minutes per ticket, the time saved is 1,200 × 3 = 3,600 minutes, or 60 hours a month. Compare that with the agent's monthly running and maintenance cost, and check that diagnosis quality is at least as good as before.
Agents act, so their mistakes can have consequences. Common risks include taking the wrong action, acting on manipulated input, exposing data to the wrong person and running up costs in loops.
Practical controls:
Industry analysts have warned about over-ambitious projects. Gartner predicted in June 2025 that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value or inadequate risk controls. This is a prediction published in 2025, not a measured cancellation rate. Gartner also described "agent washing," where existing chatbots and automation tools are rebranded as agents without substantial agentic capability.
If you are not sure which tasks to start with, our guide to AI readiness assessments vs AI pilots helps you decide.
There are three common routes to an agent:
Whichever route you choose, the same disciplines apply: a defined task, limited permissions, measured quality and a clear point where people take over.
An AI agent is software that uses a language model to pursue a goal by deciding its own next steps and using tools, such as searching, reading records or calling APIs, until the task is complete or it hands over to a person.
A chatbot answers questions within a conversation. An agent can plan a sequence of actions and use tools to complete a task, such as looking up an order, checking a policy and preparing a return for approval.
They can be, with limits: narrow permissions, approval for consequential actions, caps on steps and spending, full logging and testing on real cases before release. Start with tasks where mistakes are cheap and easy to catch.
List three tasks your team repeats every week that require looking things up in several systems. Score each on frequency, time spent and the cost of a mistake. The highest-scoring, lowest-risk task is usually the right first candidate for an agent, or for simpler automation.
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