Agentic Operating Model

Before the first agent goes live, we settle who owns it, what it is allowed to do and when a human takes over. AI rarely fails on the technology. It usually fails on unclear responsibilities.

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What we look at

From open questions to rules that hold in operation.

We settle responsibility

Who owns which agent, who reviews its output, who decides on changes. An agent without an owner is a risk, not a tool.

Then we set the guardrails

What agents may do on their own, where approvals are needed, how decisions are logged. The rules are written before operation, not in reaction to the first incident.

And we prepare the teams

Roles change when agents take over routine work. A staged introduction plan makes sure the organisation comes along instead of working against it.

What you receive

Four documents that make AI operable.

Roles & responsibility model

Who owns which agent, who reviews results, who decides on changes, defined per area of use.

Governance guardrails

What agents may do independently, where approvals are required and how decisions are logged.

Escalation & human-in-the-loop

Defined handovers to people: when an agent stops, who takes over and how the case flows back.

Introduction roadmap

Pilot, training and rollout in clear stages, so the organisation comes along instead of working against it.

FAQ

Frequently asked questions about Agentic Operating Model

What is an Agentic Operating Model?

The rulebook for productive AI: roles and responsibility per agent, guardrails for independent action, escalation paths to people and a staged introduction plan for the organisation.

Why is the technology alone not enough?

Because an agent makes decisions. Without clear responsibility, approvals and logging, every wrong decision becomes a precedent with no process behind it. The organisation has to stand in front of the agent, not clean up behind it.

When should compliance and the works council be involved?

Before the first productive agent. The operating model gives them the basis: documented rules, logging and defined human oversight, which shortens the alignment considerably.

What is the concrete result?

A roles and responsibility model, documented governance guardrails, defined escalation and approval processes, and an introduction roadmap covering pilot, training and rollout.

What happens after that?

The agents themselves are built in Automate. AI Agents & Copilots deliver what the operating model defines, and AI Governance & Compliance holds the guardrails in operation.

Other Plan services

Three more services in Plan

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

Data & AI Readiness

Positioning before the first euro: data quality, system landscape and use cases evaluated, with ROI model and prioritized roadmap.

Read more

Platform & Custom Software Design

Target architecture for platform and in-house development: what HubSpot handles, what in-house software provides, and how both interact.

Read more

CRM Strategy & Process Design

Sales, marketing and service processes are designed to be mapped in the CRM, instead of existing alongside it in spreadsheets.

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.

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