Feature Merge: Multi-Agent Architecture Implementation for Spec Kit

[niksacdev]

Multi-Agent Architecture Implementation for Spec Kit

TL;DR

I've built a production-ready multi-agent system with 6 specialized engineering roles that perfectly complements Spec Kit's spec-driven methodology. The implementation is complete for Claude Code, GitHub Copilot, and universal AGENTS.md format. I'd like to extend spec kit and contribute this via PR if it aligns with your roadmap.

Repository: https://github.com/niksacdev/engineering-team-agents

What I've Already Built

A complete multi-agent collaboration system that brings specialized engineering expertise to AI-assisted development:

Specialized Agent Roles

1. Product Manager Agent - Clarifies requirements, validates business value, creates user stories
2. UX Designer Agent - Maps user journeys, ensures accessibility (WCAG 2.1)
3. System Architect Agent - Produces Architecture Decision Records (ADRs), validates security/scalability
4. Code Reviewer Agent - Reviews implementation quality, security (OWASP), performance
5. Responsible AI Specialist Agent - Prevents bias, ensures inclusive design
6. GitOps Specialist Agent - Optimizes CI/CD, validates deployment reliability

Integration with Spec Kit Commands

/speckit.specify → Delegates to Product Manager + UX Designer agents
/speckit.plan → Delegates to System Architect agent
/speckit.implement → Delegates to Code Reviewer agent (post-implementation)
/speckit.deploy → Delegates to GitOps Specialist agent (new command)

Question-First Workflow

Agents operate collaboratively, not in isolation:

• Each agent asks clarifying questions before executing
• Agents explicitly delegate to teammates when their expertise is needed
• Creates cross-functional collaboration similar to real engineering teams

Implementation Status: ✅ Complete

What's Ready Now

• ✅ Claude Code integration - Full .claude/agents/ implementation with 6 specialized agents
• ✅ GitHub Copilot integration - Complete .github/copilot-instructions.md implementation
• ✅ Universal AGENTS.md format - Platform-agnostic agent definitions compatible with any AI assistant
• ✅ Sync Coordinator - Syncs across different agent files as developer proceeds to ensure the agent instructions are always up to date with changes in requiremetns, architecture decisions
• ✅ Documentation-centric design - Agents automatically generate persistent docs in structured folders
• ✅ Repository-referenced learning - Agents access domain knowledge through file paths (token-efficient)
• ✅ Cross-agent delegation - Agents explicitly call on teammates for specialized input

File Structure (Ready to Integrate)

.claude/agents/
  ├── product-manager.md
  ├── ux-designer.md
  ├── system-architect.md
  ├── code-reviewer.md
  ├── responsible-ai-specialist.md
  └── gitops-specialist.md

.github/agents/
  ├── product-manager.md
  ├── ux-designer.md
  ├── system-architect.md
  ├── code-reviewer.md
  ├── responsible-ai-specialist.md
  └── gitops-specialist.md

AGENTS.md
docs/
  ├── adr/
  ├── requirements/
  ├── architecture/
  └── reviews/

Benefits to Spec Kit

1. Complete SDLC Coverage

• Spec Kit provides the methodology (spec-driven development)
• Multi-agents provide the execution (specialized domain expertise)
• Together: Specifications → Planning → Implementation → Deployment with expert oversight

2. Institutional Knowledge

• Agents generate persistent documentation (ADRs, user stories, security reviews)
• Builds organizational memory beyond individual conversations
• Solves enterprise audit and compliance requirements

3. Quality Gates

• Responsible AI Specialist prevents bias and ethical issues
• Code Reviewer catches security vulnerabilities (OWASP)
• UX Designer ensures accessibility compliance (WCAG 2.1)
• System Architect validates technical decisions

4. Platform Compatibility

• Already supports Claude Code, GitHub Copilot, and universal format
• Aligns with Spec Kit's multi-platform philosophy
• Works with any AI agent that supports the AGENTS.md format

If this aligns with Spec Kit's roadmap, I'm happy to:

1. Submit a PR with the agent implementations
2. Update documentation showing agent integration with spec commands
3. Create examples demonstrating spec-driven + multi-agent workflows
4. Add tests validating agent collaboration patterns

[localden]

@niksacdev can you explain what the role of Spec Kit is here and what is missing?

[niksacdev]

hey @localden - Thanks for asking— Let me share how I think engineering-agents might complement spec-kit. If this doesn't resonate with your vision for spec-kit, please let me know:

Spec-kit provides: The workflow (constitution → specify → plan → tasks → implement)

Engineering-agents adds: Busieness-Grounding and Organization Alignment
Spec-kit will perfectly execute any specification you give it. But execution quality depends entirely on specification quality:

• Is this solving a real user problem? (Business validation)
• Will users actually use this? (UX validation)
• Is this secure for our users? (Security validation)
• Can all users access this? (Accessibility validation)
• Why this approach over alternatives? (Architectural rationale)

This is where engineering-agents enriches spec-driven development.

Optional domain-specialized advisors that enrich each phase of that workflow

The value: Organizations can customize advisors to domain specific guidelines (security policies, accessibility standards, architecture patterns) and get phase-appropriate guidance throughout the spec-kit lifecycle. These are hooks that are invoked by spec-kit workflow.

Below I am giving examples of how I think each Advisor Adds to Each Phase

In essence these are Github Copilot or Claude Agents but they are orchestrated by Spec driven development principles,

/speckit.specify

Standard output: spec.md with user stories and requirements

With advisors:

# Feature Specification: User Authentication

## Business Context (PM Advisor)
Problem: Why are we building this?
Users: Who will use this?
Success Metrics: How do we measure success?
Business Value: Why does this matter?

## User Journey (UX Advisor)
Current State: How users do this today
Future State: How this feature improves their experience
Pain Points: What frustrates users now

## Responsible AI Considerations (RAI Advisor)
Privacy: What user data is involved?
Accessibility: WCAG requirements for this feature
Bias: Any ML/AI considerations

## Requirements
[Your functional requirements, informed by advisor insights]

Value: Specs include business context, user research, and ethical considerations from the start—not just functional requirements.

---

/speckit.plan

Standard output: plan.md with implementation approach

With advisors:

# Implementation Plan

## Technical Approach
[Your chosen tech stack]

## Architecture Decision Records (Arch Advisor)
ADR-001: Why JWT over sessions?
- Context: Mobile app needs offline token validation
- Decision: JWT with refresh tokens
- Alternatives: Sessions (rejected: requires server state)
- [Documented per your org's ADR template]

## Security Checklist (Architect Advisor)
- [ ] Rate limiting implemented (OWASP A07)
- [ ] Bcrypt ≥12 rounds (OWASP A02)
- [ ] CSRF protection (OWASP A01)
- [ ] [Your org's security requirements automatically included]

## Accessibility Checklist (RAI Advisor)
- [ ] Keyboard navigation (WCAG 2.1.1)
- [ ] Screen reader support (WCAG 3.3.1)
- [ ] [Your org's accessibility checklist automatically included]

Value: Plans document WHY decisions were made and include your org's specific checklists.

---

/speckit.tasks

Standard output: tasks.md with implementation tasks

With advisors:

# Tasks

## Phase 1: Setup
- [ ] Initialize project structure
- [ ] 🔒 Configure security linting (Architect Advisor)
- [ ] ♿ Setup accessibility testing tools (RAI Advisor)

## Phase 2: Implementation
- [ ] Implement login endpoint
- [ ] 🔒 Add rate limiting middleware (Architect Advisor)
- [ ] ♿ Add ARIA labels to form (RAI Advisor)
- [ ] 📐 Create ADR for auth approach (Arch Advisor)

## Phase 3: Quality Gates
- [ ] 🔒 Run OWASP security scan (Security Advisor)
- [ ] ♿ Run accessibility audit (RAI Advisor)
- [ ] 📐 Validate against architecture guidelines (Arch Advisor)

Value: Quality tasks are distributed throughout implementation, not added at the end. Customized to your org's standards.

---

Phase 5: /speckit.implement

Standard output: Working code

With advisors:

Implementation with real-time feedback:

✅ Created login.ts
⚠️  Code Reviewer: Missing rate limiting
   → Adding rate limiter middleware
✅ Fixed

✅ Created LoginForm.tsx
⚠️  RAI Advisor: Form not keyboard navigable
   → Adding tabIndex and onKeyPress handlers
✅ Fixed

✅ All tests passing
✅ Security scan: 0 vulnerabilities
✅ Accessibility audit: WCAG AA compliant

Value: Issues caught and fixed during implementation, not in post-deployment incidents.

---

[francovp]

This is a similar implementation of the "Persona-based approach to AI-assisted software development"

[moygar84-5del]

Multi-Agent Architecture Implementation for Spec Kit

TL;DR

I've built a production-ready multi-agent system with 6 specialized engineering roles that perfectly complements Spec Kit's spec-driven methodology. The implementation is complete for Claude Code, GitHub Copilot, and universal AGENTS.md format. I'd like to extend spec kit and contribute this via PR if it aligns with your roadmap.

Repository: https://github.com/niksacdev/engineering-team-agents

What I've Already Built

A complete multi-agent collaboration system that brings specialized engineering expertise to AI-assisted development:

Specialized Agent Roles

1. Product Manager Agent - Clarifies requirements, validates business value, creates user stories
2. UX Designer Agent - Maps user journeys, ensures accessibility (WCAG 2.1)
3. System Architect Agent - Produces Architecture Decision Records (ADRs), validates security/scalability
4. Code Reviewer Agent - Reviews implementation quality, security (OWASP), performance
5. Responsible AI Specialist Agent - Prevents bias, ensures inclusive design
6. GitOps Specialist Agent - Optimizes CI/CD, validates deployment reliability

Integration with Spec Kit Commands

/speckit.specify → Delegates to Product Manager + UX Designer agents
/speckit.plan → Delegates to System Architect agent
/speckit.implement → Delegates to Code Reviewer agent (post-implementation)
/speckit.deploy → Delegates to GitOps Specialist agent (new command)

Question-First Workflow

Agents operate collaboratively, not in isolation:

• Each agent asks clarifying questions before executing
• Agents explicitly delegate to teammates when their expertise is needed
• Creates cross-functional collaboration similar to real engineering teams

Implementation Status: ✅ Complete

What's Ready Now

• ✅ Claude Code integration - Full .claude/agents/ implementation with 6 specialized agents
• ✅ GitHub Copilot integration - Complete .github/copilot-instructions.md implementation
• ✅ Universal AGENTS.md format - Platform-agnostic agent definitions compatible with any AI assistant
• ✅ Sync Coordinator - Syncs across different agent files as developer proceeds to ensure the agent instructions are always up to date with changes in requiremetns, architecture decisions
• ✅ Documentation-centric design - Agents automatically generate persistent docs in structured folders
• ✅ Repository-referenced learning - Agents access domain knowledge through file paths (token-efficient)
• ✅ Cross-agent delegation - Agents explicitly call on teammates for specialized input

File Structure (Ready to Integrate)

.claude/agents/
  ├── product-manager.md
  ├── ux-designer.md
  ├── system-architect.md
  ├── code-reviewer.md
  ├── responsible-ai-specialist.md
  └── gitops-specialist.md

.github/agents/
  ├── product-manager.md
  ├── ux-designer.md
  ├── system-architect.md
  ├── code-reviewer.md
  ├── responsible-ai-specialist.md
  └── gitops-specialist.md

AGENTS.md
docs/
  ├── adr/
  ├── requirements/
  ├── architecture/
  └── reviews/

Benefits to Spec Kit

1. Complete SDLC Coverage

• Spec Kit provides the methodology (spec-driven development)
• Multi-agents provide the execution (specialized domain expertise)
• Together: Specifications → Planning → Implementation → Deployment with expert oversight

2. Institutional Knowledge

• Agents generate persistent documentation (ADRs, user stories, security reviews)
• Builds organizational memory beyond individual conversations
• Solves enterprise audit and compliance requirements

3. Quality Gates

• Responsible AI Specialist prevents bias and ethical issues
• Code Reviewer catches security vulnerabilities (OWASP)
• UX Designer ensures accessibility compliance (WCAG 2.1)
• System Architect validates technical decisions

4. Platform Compatibility

• Already supports Claude Code, GitHub Copilot, and universal format
• Aligns with Spec Kit's multi-platform philosophy
• Works with any AI agent that supports the AGENTS.md format

If this aligns with Spec Kit's roadmap, I'm happy to:

1. Submit a PR with the agent implementations
2. Update documentation showing agent integration with spec commands
3. Create examples demonstrating spec-driven + multi-agent workflows
4. Add tests validating agent collaboration patterns