name: gl-workforce-docs description: Documentation for GreenLang AI Agent Workforce Deployment

GreenLang AI Agent Workforce Deployment

Date: November 9, 2025 Purpose: Scale development from 3 apps (30%) to 10 apps (100%) by 2028 Total New Agents: 22 development agents (+ 14 existing validation agents = 36 total)

Executive Summary

To accelerate development of the remaining 7 critical applications and achieve the $970M ARR target by 2028, GreenLang is deploying 22 new AI development agents organized into 3 specialized teams:

1. 10 Functional Role Agents - Core development workforce (architects, developers, engineers)
2. 5 Domain Specialist Agents - Expert knowledge workers (regulatory, satellite ML, LLM, supply chain, formulas)
3. 7 Application Project Managers - Orchestrators for each of the 7 remaining apps

These agents work in parallel teams alongside your existing 14 validation/audit agents to build production-ready applications 8-10× faster than traditional development.

Agent Team Structure

TEAM 1: Functional Role Agents (10 agents)

Your Core Development Workforce

How They Work: These agents work like a traditional software engineering team, each with a specialized role. You invoke them sequentially (requirements → architecture → implementation → testing → deployment → documentation) or in parallel (API development + frontend development simultaneously).

TEAM 2: Domain Specialist Agents (5 agents)

Expert Knowledge Workers

How They Work: Domain specialists provide deep expertise that crosses multiple applications. For example, the Formula Library Curator supports calculation engines across ALL applications, while the Satellite ML Specialist specifically supports EUDR (deforestation) and Carbon Market Integrity (project verification).

TEAM 3: Application Project Managers (7 agents)

Orchestrators for Each Application

How They Work: Each PM agent coordinates the 10 functional role agents and relevant domain specialists to build one specific application. They manage requirements, timelines, team coordination, and delivery for their assigned app.

How to Deploy Agent Teams

Deployment Model: Parallel Development Squads

Each of the 7 applications gets a dedicated squad working in parallel:

EUDR Squad (TIER 1 - Most Urgent):
├─ gl-eudr-pm (orchestrator)
├─ gl-app-architect → Design 5-agent pipeline
├─ gl-backend-developer → Implement agents
├─ gl-calculator-engineer → Geo-validation logic
├─ gl-data-integration-engineer → 60+ ERP connectors
├─ gl-satellite-ml-specialist → Deforestation detection ML
├─ gl-llm-integration-specialist → Document verification
├─ gl-api-developer → REST API
├─ gl-frontend-developer → Dashboard
├─ gl-test-engineer → 85%+ coverage
├─ gl-devops-engineer → K8s deployment
└─ gl-tech-writer → User guides + API docs

SB 253 Squad (TIER 1):
[Same structure, different PM and features...]

Green Claims Squad (TIER 2):
[Same structure, different PM and features...]

... (and so on for all 7 applications)

Key Insight: The same 10 functional role agents can work on multiple applications in parallel because they're specialized by function (backend, API, testing) not by domain (EUDR, SB 253).

Invocation Examples

Example 1: Starting EUDR Application Development

Step 1: Invoke PM to plan

User: "I need to start building the EUDR application. Let's create a plan."

Response: Invoke gl-eudr-pm agent to:
- Review regulatory requirements
- Create development plan
- Identify team needs
- Set milestones

Step 2: Architecture Design

User: "Design the architecture for EUDR."

Response: Invoke gl-app-architect agent to:
- Design 5-agent pipeline (Intake → GeoValidation → DeforestationRisk → DocumentVerification → Reporting)
- Define tech stack (FastAPI, PostgreSQL, Sentinel-2 ML)
- Specify API endpoints
- Create deployment architecture

Step 3: Parallel Implementation

User: "Build all EUDR agents in parallel."

Response: Invoke IN PARALLEL:
- gl-backend-developer → SupplierDataIntakeAgent
- gl-calculator-engineer → GeoValidationAgent
- gl-satellite-ml-specialist → DeforestationRiskAgent (ML model)
- gl-llm-integration-specialist → DocumentVerificationAgent
- gl-api-developer → REST API endpoints

Step 4: Testing & Deployment

User: "Test and deploy EUDR app."

Response: Invoke SEQUENTIALLY:
- gl-test-engineer → Write and run 628+ tests (85%+ coverage)
- gl-devops-engineer → Create Docker, K8s, Terraform
- gl-tech-writer → Create API docs + user guides

Example 2: Building Multiple Apps in Parallel

User: "Build EUDR, SB 253, and Green Claims simultaneously."

Response: Launch 3 PM agents in parallel:

Launch in parallel:
├─ gl-eudr-pm (coordinates EUDR squad)
├─ gl-sb253-pm (coordinates SB 253 squad)
└─ gl-greenclaims-pm (coordinates Green Claims squad)

Each PM then coordinates their squad:

EUDR Squad:
- Week 1-4: Requirements + Architecture
- Week 5-8: Backend agents implementation
- Week 9-12: ML models + API + Frontend
- Week 13-16: Testing + Deployment

SB 253 Squad (leverages GL-VCCI-APP):
- Week 1-2: CARB portal integration
- Week 3-6: State compliance engine
- Week 7-10: Assurance module
- Week 11-14: Testing + Deployment

Green Claims Squad:
- Week 1-4: LLM claim extraction (27 languages)
- Week 5-8: LCA verification (leverage Product PCF)
- Week 9-12: Compliance validation
- Week 13-16: Testing + Deployment

Agent Workflow Patterns

Pattern 1: Sequential Workflow (Waterfall)

For applications with clear phases:

Requirements → Architecture → Implementation → Testing → Deployment → Documentation

1. gl-product-manager (requirements, user stories)
   ↓
2. gl-app-architect (architecture design)
   ↓
3. gl-backend-developer + gl-calculator-engineer (implementation)
   ↓
4. gl-api-developer + gl-frontend-developer (APIs + UI)
   ↓
5. gl-test-engineer (testing)
   ↓
6. gl-devops-engineer (deployment)
   ↓
7. gl-tech-writer (documentation)

Pattern 2: Parallel Workflow (Agile Sprint)

For faster development:

Sprint 1 (Weeks 1-2):
├─ gl-product-manager → Write user stories for features 1-3
├─ gl-app-architect → Design architecture (ALL features)
├─ gl-regulatory-intelligence → Research compliance requirements
└─ gl-formula-library-curator → Prepare emission factor database

Sprint 2 (Weeks 3-4):
├─ gl-backend-developer → IntakeAgent + CalculatorAgent
├─ gl-data-integration-engineer → ERP connectors
├─ gl-api-developer → API endpoints 1-5
├─ gl-frontend-developer → Dashboard mockups
└─ gl-test-engineer → Unit tests for completed agents

Sprint 3 (Weeks 5-6):
├─ gl-backend-developer → ReportingAgent + AuditAgent
├─ gl-llm-integration-specialist → Classification features
├─ gl-api-developer → API endpoints 6-10
├─ gl-frontend-developer → Dashboard implementation
└─ gl-test-engineer → Integration tests

Sprint 4 (Weeks 7-8):
├─ gl-devops-engineer → Docker, K8s, Terraform
├─ gl-test-engineer → E2E tests, performance tests
├─ gl-tech-writer → API docs + user guides
└─ gl-product-manager → Beta customer onboarding

Integration with Existing Validation Agents

Your existing 14 agents provide quality gates AFTER development:

Development Agents (NEW - Build the apps):
gl-backend-developer → Writes code
  ↓
Validation Agents (EXISTING - Ensure quality):
gl-codesentinel → Lint + type check
gl-secscan → Security scan
gl-spec-guardian → Spec compliance
gl-exitbar-auditor → Production readiness

Complete Quality Pipeline:

1. DEVELOP (New agents)
   ├─ gl-backend-developer writes code
   ├─ gl-calculator-engineer creates formulas
   └─ gl-api-developer builds endpoints

2. VALIDATE CODE QUALITY (Existing agents)
   ├─ gl-codesentinel → Linting, type checking
   ├─ gl-secscan → Security vulnerabilities
   └─ gl-spec-guardian → Spec compliance

3. VALIDATE DATA & DEPLOYMENT (Existing agents)
   ├─ gl-dataflow-guardian → Data lineage
   ├─ gl-packqc → Package quality
   └─ gl-supply-chain-sentinel → SBOM/signatures

4. RELEASE (Existing agents)
   └─ gl-exitbar-auditor → Final GO/NO_GO decision

Timeline & Capacity Planning

Development Velocity Projections

With AI Agent Teams (Parallel Development):

Total Development Time (Parallel): 20 weeks (5 months) Total Development Cost: $3.4M-$4.8M Total Engineers Required (Parallel): 35-47 engineers

Compared to Traditional Development:

Success Metrics

Agent Performance KPIs

Development Agents:

• Code Quality: 85%+ test coverage, Grade A security (92+/100)
• Development Speed: 8-10× faster than traditional development
• Bug Rate: <5 bugs per 1,000 lines of code
• Documentation Coverage: 100% (all APIs, agents, features documented)

Project Manager Agents:

• On-Time Delivery: 90%+ of milestones hit on schedule
• Budget Adherence: Within 10% of estimated costs
• Stakeholder Satisfaction: NPS >60

Domain Specialist Agents:

• Regulatory Accuracy: 100% compliance with regulations
• ML Model Accuracy: >90% for satellite deforestation detection
• Emission Factor Coverage: 100,000+ factors from authoritative sources

Deployment Checklist

Week 1: Setup & Planning

• Read this document (9_Nov_GL_Agents.md)
• Review existing agent infrastructure (14 validation agents)
• Prioritize applications (TIER 1 → TIER 2 → TIER 3)
• Assign PM agents to applications
• Create development roadmap (Gantt chart)

Week 2-4: TIER 1 Applications (EUDR, SB 253)

• Invoke gl-eudr-pm and gl-sb253-pm to create detailed plans
• Invoke gl-app-architect for both applications
• Begin parallel implementation with functional role agents
• Weekly progress reviews with PM agents

Week 5-8: TIER 2 Applications (Green Claims, Taxonomy, Building BPS)

• Invoke gl-greenclaims-pm, gl-taxonomy-pm, gl-buildingbps-pm
• Design architectures for all 3 applications
• Begin parallel implementation
• Continue TIER 1 testing and deployment

Week 9-16: TIER 3 Applications (CSDDD, Product PCF)

• Invoke gl-csddd-pm and gl-productpcf-pm
• Design architectures
• Begin implementation
• Complete TIER 1 deployment (EUDR, SB 253)

Week 17-20: Testing, Deployment, Launch

• Complete all testing (unit, integration, E2E, performance)
• Deploy all applications to production
• Beta customer onboarding
• Production monitoring and support

Conclusion

You now have a 22-agent development workforce ready to build 7 applications in parallel:

• 10 Functional Role Agents = Your core engineering team
• 5 Domain Specialists = Expert knowledge workers
• 7 Application PMs = Orchestrators for each app

Combined with your 14 existing validation agents, you have a 36-agent workforce that can deliver $970M ARR by 2028.

Next Steps:

1. Start with TIER 1 (EUDR, SB 253) - Most urgent deadlines
2. Invoke gl-eudr-pm and gl-sb253-pm to create detailed plans
3. Launch parallel development squads for both applications
4. Use functional role agents across all applications simultaneously
5. Validate with existing quality agents at every stage
6. Deploy to production and iterate

The workforce is ready. Let's build the Climate Operating System.

🌍 Code Green. Deploy Clean. Save Tomorrow.

Document Version: 1.0 Last Updated: November 9, 2025 Next Review: Weekly during active development Maintained By: GL-ProductManager + GL-TechWriter