AI Agents & Copilots

Agents that take on tasks instead of merely answering: qualifying leads, preparing quotes, resolving tickets. Built on the platform from Build and governed by the Agentic Operating Model.

Two service agents wearing headsets seen from behind at a shared desk with monitors, one with an orange ear pad speaking into his microphone.
A woman standing beside a seated colleague points at his monitor while he types on a laptop, an orange notebook under her arm. An empty focus room seen through glass with a running monitor and laptop on a white desk, a glass of water and a jacket on a chair. Two colleagues seen from behind look at one laptop, the woman pointing at the screen with a pen while the man holds an orange notebook.
What we look at

From a first use case to an agent in operation.

We start at the process, not the model

Which workflow ties up capacity today, which decisions inside it can an agent make, where does a human have to take over. That produces the first use case, small enough to prove and large enough to be worth it.

Agents work on your systems

They read and write in the CRM, use the connected data and leave logged transactions behind. Not a shadow tool beside the platform, but part of it.

Copilots help where people decide

They prepare, summarise and suggest, while responsibility stays with the team. What the agent may do alone is set by the guardrails.

What you receive

Four outcomes, all measurable.

Productive agents

One agent per use case in operation: qualifying leads, preparing quotes, resolving tickets, measurably.

Copilots in the workflow

Preparation, summaries and suggestions directly in the CRM, with the decision staying with the team.

Human-in-the-loop

Defined handovers: where the agent reaches its limit, a person takes over, with context.

Measurable results

Every agent has metrics: completed cases, quality, escalations, visible on the dashboard.

FAQ

Frequently asked questions about AI Agents & Copilots

What is the difference between an agent and a chatbot?

A chatbot reacts to single inputs based on rules. An agent pursues a goal, makes decisions, uses systems such as your CRM and carries out multi-step tasks independently. In short: the chatbot answers, the agent acts.

Which use cases work first?

Processes with clear rules and high volume: lead qualification, quote preparation, standard service enquiries, data maintenance. The first use case is chosen so it can be proven in weeks, not quarters.

Do we keep control?

Yes. Agents get limited rights, their actions are logged, sensitive steps require approval, and defined handovers bring people in. The rules for that are set in the Agentic Operating Model.

What do we need as a precondition?

Dependable data and processes in the system, because an agent is only as good as the platform it works on. If the base is missing, Data & AI Readiness clarifies what is needed first.

Who monitors the agents in operation?

Agent Monitoring & Optimization: quality, cost and escalations are measured continuously and the agents improved accordingly. Operation rather than experiment.

Other Automate services

Three more services in Automate

If Automate is not the right starting point for you, explore the other phases of the Agentic Growth Stack as well.

AI Workflow Automation

End-to-end processes across system boundaries, with AI at the points where fixed rules are not enough.

Read more

Agent Monitoring & Optimization

What agents do is measured: quality, cost and escalations in view, with clear handovers to people.

Read more

AI Governance & Compliance

Guardrails for using AI: approvals, logging and traceability, built in from the start.

Read more
first step

The first step towards an agent-native company.

In the Enterprise Discovery Workshop we develop a clear target picture and a business case that holds up, in a matter of days. Fixed price, no open-ended day rates.

Smiling man in a dark blue suit in a bright office.
Rather talk first?
We get back to you personally.
Request a workshop
bg-leftright-cta