How software gets built when AI runs the cycle

AI software development lifecycle

With our proven AI-native SDLC, we reimagined the software development process to combine the old control objectives with new enforcement.

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steps in every project

10+

Agents doing the work

products live in production

The basics

Software delivery happens in the same steps

SDLC

THE CYCLE

01 Define — agree what the system has to do

02 Design — decide how it will be built

03 Build — write the code

04 Test — prove it works

05 Release — put it in front of users

06 Improve — fix, change, repeat

Every software project runs on the same Software Development Life Cycle, or SDLC, whether or not anyone names it. The steps are fixed, but the people and the timing are not. Two projects can follow the same SDLC and still differ in who handles each step and how long it waits before the next one starts.

Why does waiting take the longest?

Traditional SDLC

One approved feature, from decision to live:

waiting for a
free developer

building

waiting for
review

review

waiting for
testing

testing

waiting for
a release slot

work actually happening queueing, waiting for a person to be free Illustrative shape, not measured data

Usually, the work itself is quick. The delay comes from queues between steps that run up the clock and the bill.

The cause

Software team has a fixed bandwidth

THE WHOLE TEAM BA DEV DEV QA DEVOPS PM

Six people, five roles — two of them developers — and a task sits in one pair of hands at a time.

writes the requirement

02 · YOUR SIDE

Client

approves it

Dev

writes the code — two at a time

Code review

a second dev checks it

tests it

DevOps

deploys it

›

FEATURE

2 DEVS MAX

WAITING

Change request

Only two tasks can be in development at once; the rest queue outside. That waiting is dead time, because the work is ready and the people aren't available.

A bigger team isn't more bandwidth

Six people does not mean six things happening at once. One task moves through them in sequence before every hand-off.

More people ≠ more speed

Each hire adds coordination, onboarding and handovers as well as capacity.

Fixes compete with features

Testing, review and bug fixing draw on exactly the same people.

The change

AI-driven SDLC makes a difference

Same cycle

STEPS 1–2 · UNCHANGED

People decide what to build, and how

Requirements and architecture undergo mutual sign-off. Nothing starts until both are approved.

Human oversight

STEPS 3–6 · NO LONGER A QUEUE

A team of AI agents does the building

AI-generated code, review, testing, and release happen in parallel, immediately, without waiting for anyone to be free.

Role-scoped agentic team AI DLC

How AI-driven development works

AI SDLC framework

AI-DLC replaces traditional sprints with shorter 'bolts' for faster delivery. Each agent does one job. Tasks arrive at the coordinator, which decides who does what and in which order.

Coordinator

ASSIGNS ALL WORK

Checks the
requirements

Sets the
architecture

Checks design,
releases

2 AGENTS

Writes the code

3 AGENTS

Reviews the code

2 AGENTS

Writes & runs tests

2 AGENTS

The builder never approves its own work

The agent that writes the code is never the agent that reviews or tests it. The same separation of duties you would expect from a human team.

Agents work at the same time

While one part of the system is being built, another is being tested, and a third is being released.

Development process

Is AI-generated code any good?

Every AI-assisted development undergoes six comprehensive checks. Each one can send the work back.

one feature

Requirements

checked for gaps and collisions

Code

written by an agent

Code review

a different agent checks it

Tests

unit, integration, end-to-end

Design

compared pixel by pixel

Release

deployed and monitored

A failed check returns the feature to be fixed, and then it has to pass the checks again.

In a human team that much checking would be unaffordable, here each pass costs minutes.

In the AI SDLC, every check is run by a different review agents than the one who did the work. Nothing approves itself.

Real projects, live today

The result: AI autonomous development process

DRAFT — full case study coming soon

A REBUILD — THE TRADITIONAL WAY 11 months, still not delivered

11 months

THE SAME REBUILD — WITH AI SDLC 3 months, in production

3 months

START12 MONTHS

1.2 people part-time instead of 5 full-time

Six months of delivery by half an analyst, half a tester and a little oversight.

No developers at all, high software quality

Products are in production.

Clients accept the work, ask for changes, and we keep shipping.

Client names held under NDA.

The decisions stay with people

What does not change in AI DLC?

Requirements

You and our analyst agree what to build, and what "finished" means.

Architecture

Our architect decides how it must be built, and approves it before anything starts.

Acceptance

Humans review the result and accept it. Nothing is finished because an agent said so.

Accountability

Named people are answerable for the work. "The agent did it" is not an answer we will give you.

Bad requirements now produce bad software faster. That is exactly why the first two steps are still human, still slow, and still approved by name.

Match today's reality

Software engineering with AI

You approve what to build

Requirements and architecture, signed off the same way as always.

We build, check and release it

Six automated checks on every change, epic by epic.

You accept it, or ask for changes

Changes go through the same pipeline. There is no "later sprint".

The pace is set by how quickly we agree on what to build instead of how many developers are available.

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