What Is an AI Agent? A Plain-English Guide for Operators

What is an AI agent, in plain English?

An AI agent is software that is given a goal and figures out the steps to reach it. Google Cloud defines agents as software systems that use AI to pursue goals and complete tasks on behalf of users.¹ The important part is the second half: an agent takes actions as well as answering.

Three parts make something an agent:

1. A model that reasons. A large language model reads the situation and decides what to do next.
2. Tools. Access to the systems where work happens: reading documents, searching a database, filling in a form, sending an email.
3. A goal and limits. A clear task, such as "prepare this new matter for conflicts review," plus rules about what it may do on its own and what needs a person.

Anthropic draws a useful line here. In a workflow, the AI model is used inside steps a developer has fixed in advance. In an agent, the model directs its own process and decides which tools to use.² Many real systems combine the two.

How is an AI agent different from a chatbot or traditional automation?

A chatbot answers, traditional automation repeats, and an agent works toward an outcome. The differences matter because vendors use the word loosely.

Be skeptical of labels. Gartner calls the rebranding of existing assistants, robotic process automation and chatbots without substantial agentic capabilities "agent washing," and estimated in 2025 that only about 130 of the thousands of vendors claiming agentic AI were offering the real thing.³

What can an AI agent actually do in a back office?

The best fits are repetitive tasks that still need some reading and judgment: work that is too varied for a rigid script but too routine for your most experienced people.

• Client intake at a law firm: read the inquiry, pull out the parties, check them against existing clients, open a draft matter and send it to an attorney for approval. See more on AI for law firms.
• Loan document review at a bank: check an application package for missing pages, extract the figures and flag anything that doesn't match.
• Prior authorization prep in healthcare: gather the records a payer asks for and assemble the request for staff to review and submit.
• A searchable knowledge base: answer staff questions from the firm's own policies, templates and past work, with links to the source documents.

Client intake and an internal knowledge base are two of the most common first workflows we build at Soren AI. At one law firm, intake that used to take three days now takes about 20 minutes.

When don't you need an AI agent?

When the task follows the same steps every time. If the inputs are clean and the rules never change, a simple, predictable workflow is cheaper, faster and easier to audit.

The AI labs say the same. OpenAI's guide to building agents notes that for some tasks a deterministic solution may suffice,⁴ and Anthropic recommends starting with the simplest approach that works, because agentic systems are often slower and cost more in exchange for better task performance.²

A good test: if you could write the process down as a checklist with no "it depends," you probably don't need an agent.

How many companies are actually using AI agents?

Fewer than the hype suggests, and mostly large ones. In McKinsey's 2026 State of AI survey, 40% of respondents at organizations with more than $1 billion in revenue reported scaling AI agents, up from 27% a year earlier. Among respondents at smaller organizations, the share held flat at 22%.⁵

In August 2025, Gartner forecast that up to 40% of enterprise applications would include task-specific agents by 2026, up from less than 5% at the time.⁶ It also predicted that more than 40% of agentic AI projects will be canceled by the end of 2027 because of rising costs, unclear business value or weak risk controls, and advised pursuing agentic AI only where it delivers clear value.³

What are the risks of AI agents?

The main risks are agents acting beyond their authority, being manipulated by what they read, and mistakes nobody catches. FINRA's 2026 oversight report warns that agents may act beyond the user's actual or intended scope and authority, and suggests firms consider where to put human-in-the-loop oversight.⁷

Security is a real concern too. In NIST testing published in January 2025, the strongest new attack that hides malicious instructions in data an agent reads succeeded 81% of the time, versus 11% for the strongest baseline attack.⁸

The controls that help are practical:

• Least access: give the agent only the systems and permissions the task needs, starting read-only where possible.
• People approve what matters: anything sent to a client, filed, paid or recorded goes to a person first.
• A full log: keep a record of what the agent read, decided and did, so you can review and explain it.
• Know where data goes: map where prompts, documents and logs are processed and stored. For banks, we covered how to keep customer data in-region.

How should an operations team start with AI agents?

Start with one workflow. Pick a task that is frequent, well understood and easy to measure.

1. Map the workflow as it really runs, including the exceptions and the systems it touches.
2. Decide what the agent may do alone and what always goes to a person.
3. Start read-only or draft-only, so the agent prepares work and people send it.
4. Measure one thing, such as turnaround time or hours saved, against how the work ran before.
5. Expand only after it holds up on real work for a few weeks.

At Soren, engineers spend the first two weeks interviewing the people who do the work, and the first workflow is typically live within two to three weeks of getting access to the systems it needs. More on how we work, and on how this differs from traditional consulting.