Use case · AI agents

We want AI that does the task. Not just answers questions about it.

An AI agent reads a request, decides the next step, and takes it in your tools. That might mean updating a record, drafting a reply or booking a follow-up. It works best on clear, repeated tasks, with a person approving the risky steps.

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Sounds familiar? You are in good company.

Sales

“Every new lead needs research, a CRM entry and a first email. It eats our mornings.”

Support

“Half our tickets mean looking up an order and sending a standard update.”

Operations

“Someone checks three systems every morning just to see what needs doing.”

Scheduling

“We spend hours booking, moving and confirming meetings by email.”

From a few lines to a clear brief.

This is the kind of thing a company types into Willo, and the brief that comes out of it.

What the company typed

“When a new lead comes in, someone looks them up, adds them to the CRM and writes a first email. We'd like AI to do most of that and let us approve the email.”

ProblemEach new lead needs manual research, a CRM entry and a first email before anyone talks to them.

Outcome wantedAn agent that researches the lead, fills in the CRM record and drafts the email for a person to approve.

Systems involvedWebsite contact form, CRM, shared email, public company information.

Kind of expertAI agent builder who has connected agents to a CRM and designed approval steps.

First call would coverWhich steps are safe to hand over, where a person must approve, how to test on past leads, and what to measure.

Illustrative example — not a client case.

What a good expert does here. Narrow job, clear limits.

01

Defines the job tightly

A good agent has one narrow task, a short list of tools it may use, and a clear point where it stops.

02

Puts approvals in the right places

Anything that sends, pays or deletes waits for a person. Reading and drafting can run on their own.

03

Tests on real history

They run the agent on past cases and compare its choices with what your team did. Then you decide what to switch on.

Before the first call

What to have ready

The steps a person takes today, even as a rough list
Which tools the agent would read from and write to
How often the task happens per day or week
Who would approve the agent's actions

Be honest about

Things to watch

Agents make mistakes. Limit what they can change without approval.
Give the agent its own account with only the access it needs.
Keep a log of every action, so you can see what it did and why.

Questions about AI agents, answered.

What is the difference between an AI agent and an automation?

A standard automation follows fixed rules: when X happens, do Y. An agent can read the situation and choose between steps. Many good setups use both, with the agent only where judgment is needed.

Is it safe to let an AI act in our systems?

It can be, with limits. A good expert gives the agent only the access it needs and makes it ask before anything that sends, pays or deletes. Start small and widen access once you trust the results.

Do we need to know which AI model to use?

No. Choosing the model and tools is part of the expert's job. You only need to describe the task and the systems involved.

Can an agent work with our own internal tools?

Often, if the tool has an API, which is a way for software to talk to other software. If it does not, the expert will explain the other options on the first call.

Related use case

Describe the task you'd hand to an assistant.

A few sentences is enough. A real person reviews the brief, and you get 2–3 matched experts, usually within 3–7 days. The first step is a short call with no commitment. Free for companies.

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