AI-native development, anchored in your existing team.
We introduce Claude Code, Codex, and comparable tools where they have a measurable effect, with process, quality gates, and a team that owns the new way of working.
The situation
Many engineering teams already use AI coding assistants as an individual tool for individual developers, without a shared process. The result is inconsistent: faster lines of code, but no reliable statement about quality, test coverage, or maintainability. Leadership is left with a gut feeling instead of a metric.
For us, Agentic Engineering means AI-assisted development as an operating model, not a single tool. We align toolchain, code review, CI/CD, and quality gates so AI agents can contribute productively without quality or traceability suffering. That also means being honest about where agents still fail today, for example on complex, poorly documented legacy systems.
The difference from a tool rollout: we stay with the team until the new way of working holds, measured against your own metrics such as change cycle time, production error rate, and review effort, not against a promised multiplier.
Deliverables
Toolchain assessment
An assessment of where Claude Code, Codex, or Cursor make sense in your codebase and process, and where they don't.
Quality gates for AI-generated code
Defined criteria in CI/CD: tests, linting, review rules, and approval processes that apply to code written by agents.
Measurement system for throughput and quality
Concrete metrics (such as change cycle time, post-release error rate, review time) as a baseline and ongoing measurement.
Trained team
Your developers work confidently with the tools, know the limits, and know when an agent isn't the right choice.
Playbook
Documented conventions, prompts, review rules, and escalation paths that stay with the team once we're gone.
Our approach
- 01
Measure the baseline
We capture today's cycle time, error rate, and review load before changing anything. Without a baseline, you can't show an effect.
- 02
Select a pilot area
We choose a bounded part of the codebase with the team where the new way of working can be tried without major risk.
- 03
Set up toolchain and gates
We set up coding assistants, CI/CD rules, and review processes for that area and adjust them while in operation.
- 04
Train the team
We work in a pair-programming setup with the team instead of running training disconnected from practice. Conventions emerge on real code.
- 05
Roll out and measure
We carry the way of working to further areas and continuously compare metrics against the baseline.
Typical starting points
| Starting point | Our approach |
|---|---|
| Individual developers use copilots without a shared standard. | A team standard for prompts, review, and quality gates. |
| Leadership has no metric for the effect of AI tools. | A measurement system with a baseline and ongoing tracking. |
| Usage drops off again after a tool rollout. | Anchoring in the process instead of leaving it voluntary, with training on real code. |
| Legacy code is considered “not AI-suitable.” | An honest assessment of which parts are suitable and which aren't. |
Our approach: A team standard for prompts, review, and quality gates.
Our approach: A measurement system with a baseline and ongoing tracking.
Our approach: Anchoring in the process instead of leaving it voluntary, with training on real code.
Our approach: An honest assessment of which parts are suitable and which aren't.
Outside our scope
We don't promise a blanket speed multiplier. The effect depends on codebase, team, and process, and is measured, not claimed. We don't introduce tools your team can't keep operating on its own, and we don't replace developers with agents. And we don't take on general process consulting unrelated to code.
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.
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 toolchain and tell you honestly where Agentic Engineering already pays off today.