From traditional SDLC to AI-native

Book a call See the way there

What we see today

Belief Part of the team dismisses AI before trying it, and another part expects miracles. That is the same mistake in two directions.
Capability No training, too little access to AI tooling, and processes built for a world where writing code was the expensive part.
Foundation Most product knowledge lives in people's heads. AI workflows are fragmented, each only as good as whoever built it.
Adoption The spread between teams is huge: some work almost AI-first, others barely use AI. There is no common way of working to close that gap.

The way there

We build on what Anthropic publishes about AI-native engineering. The bottleneck does not disappear, it moves: in the end, to the moment someone decides.

Anthropic: The AI-native SDLC playbook

Today

Six desks, five handoffs. Build takes the longest, and it sets the pace for the whole chain.

  1. Product
  2. Analysis
  3. Build
  4. Review
  5. Test
  6. Release

AI-assisted

Agents build. The bottleneck moves to what you asked for and to whoever checks it.

  1. Product
  2. Analysis
  3. Build
  4. Review
  5. Test
  6. Release

AI-native

One run, no handoffs. A person still decides four times, and that is the only thing still holding the work up.

  1. Product
  2. Analysis
  3. Build
  4. Review
  5. Test
  6. Release
What happens in production feeds the next piece of work
Four gates
  1. 1Intent accepted
  2. 2Spec approved
  3. 3Change approved
  4. 4Release authorised
  • Where the run narrows, the work waits
  • Gate: a person decides

Where the real work is

Most of this is not technical. It is bringing people along, and building a way of working on the culture and the habits you already have.

Bringing people along

“We have always done it this way” is not answered by a memo. We put someone in the team who has already made the move, and show the difference in a real repository, against what the team would have shipped itself.

Built on your own culture

There is no standard process we come and install. We start from how your teams work today and from what already works well, and build on that.

Quality gates

We build quality gates into your CI/CD, so quality becomes visible and automatic, and every team holds to today’s standards, or stricter. Nobody reviews by hand the volume an agent produces: the quality gates take the volume, your reviewers judge the decisions. Access without gates buys you more code and less confidence in it.

One way of working

We help a small platform team of your own set the agreements down in the repository and carry them into every team through the tooling itself, not through a wiki page written once and never read again. It is a baseline, not a limit: each team builds on it, and feedback from every team adjusts it.

Training

People who use AI every day still ask us what MCP is, use the agent as a search box, and point it at small chores instead of at whole workflows. The willingness is there, the knowledge is not yet. We train your engineers on agentic workflows, not on one-off prompts.

Where does your team stand today?

Tell us briefly how your teams work today and where it is rubbing. We say which step comes first, and why it comes first.

Rather call? +32 11 11 10 20 Or mail [email protected] Beringen, Belgium

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