How TechWolf built a job architecture using its own AI agent

Jeroen Van Hautte
April 16, 2025
•
3 min read
Two colleagues at a desk reviewing code on a large monitor, one pointing at the screen
Contents
[ TL;DR]

TechWolf grew fast—and fast growth meant new challenges: clarity on roles, internal mobility, and fair pay. Instead of spending months manually mapping a job architecture, we used our own AI Agent to build one in just two weeks.


What we did:

  • Identified 24 unique roles across manager and IC tracks

  • Used AI to define skills and job levels

  • Partnered with team leads for validation

  • Built five-level skill frameworks per role

  • Delivered everything in a format ready for action

The result: Clear expectations, better development conversations, and a system that updates with the business—thanks to AI.


Why TechWolf needed a job architecture

By early 2025, TechWolf had crossed the 100-employee mark. We had talented people in 10 departments, but no standard way to describe roles, guide promotions, or ensure fair compensation.

We knew what we needed:

  • A clear structure for growth

  • Consistent expectations for every role

  • A fair and future-proof compensation model

Our VP of People was staring down a challenge many HR teams face—traditional job architectures are slow to build and quick to go out of date. Meanwhile, our product team had just launched a beta version of what is now the Job Architecture Redesign Agent, which helps customers do exactly this. So we decided to become our own beta customer.

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How we built it — In just 5 steps

1. Identified the roles

We started with roles that had multiple employees, which gave us 24 unique jobs across IC and manager tracks. Each role would have five levels—providing clarity for both progression and pay.

2. Fed the data to our agent

We combined internal documents with market labor data. Then, the Job Architecture Redesign Agent inferred 5–10 core skills for each role.

3. Validated with leaders

Next, team leads reviewed and refined the skill lists. We kept only the most important ones.

4. Generated job grading

Our AI mapped out five job levels from most junior to senior for each selected skill, helping define what “growth” looks like in every role.

5. Finalized with human input

Using the AI-generated draft, each department head fine-tuned their job architecture. Everything was delivered in a structured Excel doc that teams could use immediately.

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Our job architecture is key in TechWolf's scaling journey. Being able to use our own AI Agent not only helped us get to a great result -- it got us there in just two weeks.
Jeroen, CTO TechWolf

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The Result

  • Week 1: Skills inferred and validated

  • Week 2: Frameworks finalized and rolled out

That’s it. In a matter of weeks, every TechWolf team member had a clear view of:

  • What’s expected of them

  • How they can grow

  • How their role impacts compensation

And the best part? As the business evolves, our Agent keeps the architecture up to date. Changes take hours, not months.

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I’ve done job architectures before. They’re usually long, frustrating, and the output gets outdated quickly. With TechWolf, it took two weeks. You’re not just getting speed—you’re in control. The AI makes suggestions, you make the decisions. Plus, with real-time market data, we now have a future-proof, living framework. This is what job architecture will look like 10 years from now. We just did it today.
Giovanni Lotti, VP People at TechWolf

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A closer look: Engineering framework example

To show you what this looks like in practice, here’s a snapshot from our engineering department—the largest in the company.
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Each role includes:

  • A list of 5–10 core skills

  • Job grading from 1 (junior) to 5 (senior)

  • Clear distinctions between IC and manager tracks

This framework isn’t just for HR. Engineers now use it in performance reviews, career development conversations, and team planning.

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Ready to Try It Yourself?

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Want to see how your org can move from “outdated and unclear” to “living and transparent”?
The Job Architecture Redesign Agent is now generally available as part of our Summer '26 launch. Talk to us and build your job architecture with AI.

👉 Explore the Engineering Framework

‍See how TechWolf’s AI-powered job architecture comes to life—with real role examples, defined skill sets, and growth levels that go beyond HR.

Download here
Download here

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Using AI while interviewing at Techwolf

At TechWolf, we see generative AI as part of the modern toolkit — and we expect candidates to treat it that way too. We love it when people use AI to take their thinking to the next level, rather than to replace it.You are welcome to use tools like ChatGPT, Claude, or others during our interview process, especially in take-home assignments or technical exercises. We encourage you to bring your full toolkit — and that includes AI — as long as it reflects your own thinking, decisions and creativity.We don’t see AI as replacing your skills. Instead, we’re interested in how you use it: to brainstorm ideas, speed up iteration, validate your thinking, or unlock new ways of approaching a challenge. Great candidates show judgment in when to rely on AI, how to adapt its output, and where to go beyond it.

What we’re looking for:

Our interviews are designed to understand how you think, solve problems, and express ideas. Using AI in a way that amplifies those things — not masks them — is encouraged.

What to avoid:

We ask that you don’t submit AI-generated work without review, or present answers that you can’t fully explain. We’re not testing the model — we’re getting to know you, your skills, and your potential. If there are cases where we don’t want you to use AI for something, we’ll tell you ahead of the interview being booked.In short: use AI as you would on the job — as a smart assistant, not a stand-in.

Example: Programming with AI

In a coding challenge, you’re welcome to use generative AI to support your workflow — just like you might in a real development environment. For instance, you might use AI to quickly generate boilerplate code, look up syntax, or get a first-pass solution that you then adapt and debug collaboratively. What we’re interested in is your ability to reason through trade-offs, communicate clearly, think about complexity and iterate effectively — not whether you memorized the syntax perfectly. If using AI helps you stay in flow and focus on higher-level problem-solving, we consider that a strength. There could be some challenges where we won’t allow you to use AI - in that case we’ll tell you in advance, and will tell you why.