AI coding agents are getting incredibly good at writing code.
But there is another problem that becomes more obvious as projects get larger:
Who manages the engineering process around the AI?
When working with AI coding agents, we often end up repeatedly providing project context, explaining architecture, reminding the AI about conventions, reviewing generated code, and manually coordinating different parts of development.
I wanted to solve that problem.
So I built AI Engineering Orchestrator, a VS Code extension designed to add an engineering workflow layer around AI coding agents.
The idea
Instead of treating an AI coding agent as a single developer, the Orchestrator treats software development more like an engineering team.
For example:
Requirement
↓
Business Analyst
↓
Solution Architect
↓
UI/UX Designer
↓
Backend Developer
↓
Frontend Developer
↓
QA Engineer
↓
Security Engineer
↓
Code Reviewer
↓
Release
Not every task needs every role.
A small bug fix might simply be:
Analyze
↓
Implement
↓
Test
↓
Review
While a large feature can go through a much more complete workflow.
Roles
The extension provides specialized engineering roles such as:
- Product Manager
- Business Analyst
- Solution Architect
- Software Architect
- Database Architect
- UI/UX Designer
- Frontend Developer
- Backend Developer
- DevOps Engineer
- QA Engineer
- Security Engineer
- Performance Engineer
- Code Reviewer
- Technical Writer
- Release Engineer
Custom roles can also be created.
The important part is that a role defines what the AI should focus on, what it is allowed to do, and what quality requirements it needs to follow.
Skills
Roles can use reusable skills instead of having one huge system prompt.
For example, a backend task might activate:
API Design
Validation
Authentication
Error Handling
Database Design
Testing
Security
A frontend task could activate:
Component Design
State Management
Accessibility
Responsive Design
Performance
Testing
Only relevant skills should be included for the current task.
The biggest focus: context optimization
One of the biggest problems with AI-assisted development is context.
A project might contain hundreds or thousands of files, but a particular task may only require five of them.
Instead of sending the entire project, the Orchestrator tries to identify the most relevant context.
For example:
Current Task
+
Current File
+
Relevant Function
+
Direct Dependencies
+
Related Tests
+
Relevant Architecture
+
Relevant Decisions
+
Relevant Conventions
The result is a smaller, focused context package.
The goal isn't simply to reduce tokens.
The goal is to provide the AI with better context per token.
Project memory
The extension maintains project knowledge so AI agents don't have to rediscover the same information repeatedly.
The project can maintain information such as:
Project Architecture
Requirements
Coding Conventions
API Contracts
Database Information
Testing Strategy
Security Rules
Tasks
Architectural Decisions
For example:
DEC-014
Decision:
Use PostgreSQL.
Reason:
The application requires relational transactions.
Affected:
Backend
Database
Infrastructure
A future task can retrieve the relevant decision instead of receiving the entire history of previous conversations.
Structured workflow
The Orchestrator uses workflow phases such as:
DISCOVER
↓
ANALYZE
↓
PLAN
↓
ARCHITECT
↓
DESIGN
↓
IMPLEMENT
↓
TEST
↓
SECURITY
↓
REVIEW
↓
DOCUMENT
↓
RELEASE
The workflow is dynamic.
Irrelevant phases can be skipped.
Quality gates
AI-generated code shouldn't automatically be considered production-ready.
The Orchestrator can introduce quality gates such as:
Architecture Gate
Implementation Gate
Testing Gate
Security Gate
Code Review Gate
Documentation Gate
For example, implementation may require:
✓ Type checking
✓ Linting
✓ Tests
✓ Acceptance criteria
before the task is considered complete.
AI-provider agnostic
The idea is not to replace AI coding agents.
Instead, the Orchestrator is designed as a layer around them.
The architecture is provider-oriented so different coding agents can be integrated through adapters.
The long-term goal is to work with different AI coding workflows rather than locking developers into a single AI provider.
Security and guardrails
AI agents can make powerful changes, so permissions matter.
Different roles can have different capabilities.
For example:
Backend Developer
Can:
✓ Read files
✓ Create files
✓ Modify backend code
✓ Run tests
Cannot:
✕ Modify production database
✕ Expose secrets
✕ Change architecture without approval
The extension is also designed around secret detection, controlled execution, diff review, and approval for potentially destructive operations.
What I'm trying to build
The bigger vision is:
The AI should do the coding. The Orchestrator should decide what the AI needs to know, which role it should act as, what it is allowed to do, and how the result should be validated.
This makes the extension less like another AI chatbot and more like an engineering operating system for AI coding agents.
Try it
AI Engineering Orchestrator is available for VS Code:
I'd love to hear from developers who are already using AI coding agents.
What is the biggest problem you face today: context, consistency, architecture, token usage, testing, or managing the AI workflow?
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