What we often see
Conviction
Part of the team wants nothing to do with it, another part expects miracles. Neither helps to introduce it well.
Skills
Little training, limited access to the right tools, and processes designed for a time when writing code was the expensive part.
Foundation
Product knowledge sits with a few people. Specifications and documentation are missing — so agents lack exactly the context they need.
Adoption
Everyone uses their own tools and prompts. The gap between teams and people grows, and there's no shared way of working to close it.
The path
Three steps, one moving bottleneck
When agents take over the building, the work doesn't disappear — it moves. If you don't see that coming, you mainly get more code to review.
Traditional
- Analysis
- Build
- Review
- Test
- Release
Many handovers between roles. Building is the slowest step, so that's where all the attention goes.
AI-assisted
- Specify
- Build
- Review
- Test
- Release
Agents help build. The bottleneck shifts to specifying well and reviewing thoroughly.
AI-native
- Goal
- Specification
- Agents build & test
- Change
- Release
One continuous flow. Agents build, test and document; people decide at four fixed points.
People decide, agents execute
Four decision points
Speed without control is a risk. In an AI-native process the human decisions are fixed — and everything in between may be automated.
- 01
Goal accepted
Are we solving the right problem, and when is it done?
- 02
Specification approved
What exactly are we building, and how will we know it works?
- 03
Change approved
Does the code do what the specification asks — secure, tested and maintainable?
- 04
Release cleared
Ready for production, with a way back if something still goes wrong.
Where the work is
Bringing people along
Especially experienced developers who spent years refining how they work. It won't succeed without them, and persuasion alone won't do it either: they need to see for themselves that it works.
Starting from what's there
We don't install a standard process. We build on the culture, tools and agreements your team already has.
Quality gates in CI/CD
Tests, static analysis, security scans and review rules that run automatically on every change. Quality becomes visible and can't be bypassed — not even by an agent.
One shared way of working in the repository
Instructions, conventions and agent configuration live in version control, next to the code. Everyone works the same way, and the method improves along with the product.
Training engineers in agentic workflows
Not in one-off prompts, but in working with agents: scoping tasks, providing context, specifying, judging output.
Security and data
Which code and which data go to which model, how to handle secrets, and what an agent may execute in your environment. This is where our security background pays off.
Why Leart
- Developers ourselves. 25+ years of software development, with certifications in Java, React, Kubernetes, AWS and AWS Bedrock, among others. We talk to your team about code, not slides.
- Experience with large teams. At large enterprises and government bodies you learn what happens when a way of working has to roll out across dozens of developers.
- Security from the start. AI-native development without exposing your codebase or your data.
- Tool-independent. We don't take sides in the AI tool wars. The way of working has to keep working when you switch tool or model.
Who it's for
Software teams and IT departments already experimenting with AI tools that want to move to a shared, manageable way of working. And smaller development teams that want to know where to start.
Contact
Where does your team stand today?
Briefly describe how you work today and where it gets stuck. You'll get honest advice on which step will deliver the most — and why.
You'll get a reply within one working day.
- Emailinfo@leart.be
- Phone+32 89 24 33 50
- LinkedInFollow us on LinkedIn
- RegionBilzen-Hoeselt · Limburg, Belgium
- CompanyLeart · BE 0769.426.863