Case 03 / AI orchestration

AIDEVELOPMENTSYSTEM

The system automates the development loop: a lead AI plans and reviews the work, an implementation AI writes code, and a quality layer prevents unchecked changes from passing.

Challenge

Make AI-assisted development sequential and controlled

A single model can write code quickly, but without stable context it may lose requirements, miss issues or optimize a local change at the product's expense.

The roles are separated: the lead model preserves the goal and decomposes the work, the implementation model completes tasks, and every result goes through automated checks.

Your path to a solution6 clear steps

01Contact meYou describe the challenge

02DiscoveryWe identify the cause and goal

03Solution designWe define the exact system

04DevelopmentI build the working product

05LaunchWe validate it in real use

06Problem solvedThe process works and can evolve

Loop / 06

How two AI models move a product from task to verification

Task direction

The lead model turns a product goal into a specific task with constraints and completion criteria.

Product context

Before implementation, the system gathers the files, decisions and requirements relevant to the current change.

Code implementation

The implementation model completes the task within its defined scope and records which parts were changed.

Result review

The lead model compares the code against requirements, architecture and the intended user journey.

Automated checks

Builds, types, tests and structural rules become a mandatory gate before acceptance.

Product improvement

When a necessary feature is identified, the lead model defines it as a separate task and oversees its implementation.

Quality control / 06

Roles and checks are not mixed in one prompt

Every stage leaves a verifiable result. This preserves product context and makes it clear why a change was accepted or returned.

Lead AI

Preserves the goal, plans tasks and decides whether work is ready.

Developer AI

Writes code from a precise brief without expanding scope without reason.

Quality gate

Runs mandatory checks and blocks results when errors remain.

Completion criteria

Connects every change to clear product behaviour requirements.

Decision history

Preserves the work log, review notes and reasons behind changes.

Human control

Keeps final approval for meaningful architectural decisions with the product owner.

Outcome

Code is produced inside a controlled engineering loop

Instead of one-off generation, the system creates a sequence: task, context, implementation, review, verification and improvement. AI becomes a useful development participant without receiving uncontrolled authority over the product.

This description reflects the process architecture; speed and quality claims require separate measurement and are not invented here.

Technology stack

TypeScriptLLM OrchestrationCode ReviewTest RunnerGitQuality Gates

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