Isn't this the same as Agentic Engineering?

Agentic Engineering introduces tools and quality gates into an existing team. The operating model goes further: what roles, planning, and governance look like permanently once agents are a fixed part of capacity.

For what company size is this relevant?

From the size at which several teams work independently with agents and inconsistency becomes a problem, usually from a double-digit number of developers.

How does the role of tech leads change?

Tech leads shift more toward review, architecture decisions, and maintaining the skills agents work with, less toward implementing routine tasks themselves.

What happens if we don't introduce the model?

The organization stays with individual, inconsistent usage. That limits the effect and makes it harder to demonstrate quality and accountability, for example to an investor or advisory board.

An operating model for teams that work with agents.

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When agents permanently take on partial tasks, roles, planning, and governance change. We build the operating model that carries that change.

The situation

Introducing an AI coding assistant is a tool change. Running agentic development permanently is an organizational question. Once agents regularly complete tasks on their own (implementation, tests, research, documentation), roles shift: who reviews and is accountable for what an agent delivers? Who maintains the prompts and skills that apply to the codebase? How is capacity planned once part of the work depends on agents rather than person-days?

Most companies answer these questions implicitly and inconsistently, because they introduced the tool first and let the organization catch up afterward. We reverse the order: we design the operating model (roles, decision rights, review and escalation processes, capacity planning) and align the toolchain to it.

The result is an organization where it's clear who is accountable for agent-produced work, how quality is assured, and how the structure evolves with growing maturity, from individual pilots to an engineering organization where agents are a natural part of capacity.

Deliverables

Role model
Clear responsibilities for maintaining prompts and skills, reviewing agent-produced work, and escalating errors.
Decision rights and approval processes
Defined boundaries for which changes an agent may make without human approval and which it may not.
Capacity and planning model
An approach for how sprint or roadmap planning accounts for agent capacity alongside person-days, without overestimating it.
Governance metrics
Metrics that let leadership continuously assess the state of the agentic organization, not just the individual pilot.
Maturity path
An assessment of what stage your organization is at today and what the next sensible step is.

Our approach

01
Current-state assessment
We capture how teams work with AI tools today, which roles already carry de facto responsibility for agentic work, and where that's unclear.
02
Design the target model
We design the role and process model with leadership, matched to the size and maturity of the organization.
03
Pilot
We test the model in one team before it applies company-wide, and adjust it to real friction points.
04
Anchor it
We fold the model into planning processes, leveling, and responsibilities so it outlasts the pilot.

Typical starting points

Starting point	Our approach
Every team uses agents differently, without a shared standard.	A company-wide role and process model.
It's unclear who is accountable for errors in agent-produced code.	Defined review and approval processes with clear responsibilities.
Planning counts agent capacity like additional person-days.	A realistic capacity model with limits and assumptions.

Every team uses agents differently, without a shared standard.

Our approach: A company-wide role and process model.

It's unclear who is accountable for errors in agent-produced code.

Our approach: Defined review and approval processes with clear responsibilities.

Planning counts agent capacity like additional person-days.

Our approach: A realistic capacity model with limits and assumptions.

Outside our scope

We don't deliver a generic off-the-shelf organizational model or a blueprint copied from another company. We don't take on general HR or compensation consulting. Where leveling or compensation are touched, we raise the topic, but implementation stays with your HR function. And we don't build a model meant to work without accompanying toolchain work.

Frequently asked questions

	Ausgangslage	Umsetzung	Ergebnis
EdTech company, engineering organization	Engineering team used AI coding tools individually, without a shared workflow.	Toolchain, adapted workflow, and quality gates for AI-assisted development.	The team works agentically as the default, with controls that fit the new way of working.
EdTech company, PE-financed	PE-financed EdTech company, technical picture unclear before the investment.	Tech DD, greenfield architecture, AI agents in production, AI-assisted engineering organization.	The company runs AI in day-to-day operations, not just in pilot projects.

EdTech company, engineering organization

Ausgangslage

Engineering team used AI coding tools individually, without a shared workflow.

Umsetzung

Toolchain, adapted workflow, and quality gates for AI-assisted development.

Ergebnis

The team works agentically as the default, with controls that fit the new way of working.

EdTech company, PE-financed

Ausgangslage

PE-financed EdTech company, technical picture unclear before the investment.

Umsetzung

Tech DD, greenfield architecture, AI agents in production, AI-assisted engineering organization.

Ergebnis

The company runs AI in day-to-day operations, not just in pilot projects.

Initial call: 30 minutes, concrete.

We look at your current organization and tell you which stage of the agentic operating model you're at.