ADR-027-v4: Implementation Coordination Framework (Part 1: Narrative)

Document: ADR-027-v4-implementation-coordination-framework-part1-narrative
Version: 1.0.0
Purpose: Define multi-agent coordination for ADR-to-deployed-production transformation
Audience: Project stakeholders, development teams, AI coordination architects
Date Created: 2025-09-01
Date Modified: 2025-09-01
Status: DRAFT

Table of Contents

• 1. Executive Summary
• 2. Multi-Agent Coordination Architecture
• 3. Implementation Pipeline Stages
• 4. Agent Specializations and Dependencies
• 5. Code Generation and Testing
• 6. Build and Container Strategy
• 7. Deployment and Infrastructure
• 8. Monitoring and Operations

↑ Back to Top

1. Executive Summary

The Technical Challenge

CODITECT v4 requires coordinating 15 specialized AI agents to transform 26 Architecture Decision Records into a fully deployed production system. Each ADR contains detailed technical specifications that must be implemented, tested, containerized, and deployed without human intervention.

Coordination Framework Overview

ADR-027 establishes a systematic approach where:

• Agents work in waves based on dependency graphs
• File-level claiming prevents conflicts
• Continuous integration validates each component
• Automated deployment pipelines ensure production readiness
• Real-time monitoring enables rapid issue detection

Implementation Flow

↑ Back to Top

2. Multi-Agent Coordination Architecture

ORCHESTRATOR Role

The ORCHESTRATOR agent serves as the central coordinator:

• Parses all 26 ADRs to identify components
• Creates dependency graphs between components
• Assigns work to specialized agents
• Monitors progress via CODI logging system
• Manages handoffs between agents
• Enforces quality gates at each stage

Agent Communication Protocol

Communication_Channels:
  CODI_Logs:
    - Real-time status updates
    - File claim notifications
    - Progress percentages
    - Completion signals
    
  File_System:
    - Code artifacts
    - Test results
    - Build outputs
    - Configuration files
    
  Git_Repository:
    - Version control
    - Branch management
    - Merge coordination

Conflict Prevention Mechanisms

↑ Back to Top

3. Implementation Pipeline Stages

Stage 1: Specification Analysis

Each agent begins by analyzing relevant ADRs:

class SpecificationParser:
    def extract_requirements(self, adr_number):
        # Parse ADR technical specifications
        # Identify interfaces and contracts
        # Extract test requirements
        # Determine dependencies
        return ComponentSpecification

Stage 2: Code Generation

Agents generate code following patterns from foundation standards:

// Example: DATABASE_SPECIALIST output
pub struct UserRepository {
    db: Arc<Database>,
    tenant_id: TenantId,
}

impl UserRepository {
    pub async fn create(&self, user: User) -> Result<User> {
        let key = format!("{}/users/{}", self.tenant_id, user.id);
        self.db.transact(|txn| {
            txn.set(&key, &user)?;
            Ok(user)
        }).await
    }
}

Stage 3: Test Generation

Every component includes comprehensive tests:

#[cfg(test)]
mod tests {
    use super::*;
    
    #[tokio::test]
    async fn test_user_creation() {
        let repo = UserRepository::new_test();
        let user = User::new("test@example.com");
        
        let created = repo.create(user.clone()).await.unwrap();
        assert_eq!(created.email, user.email);
        
        // Verify tenant isolation
        let other_tenant_repo = UserRepository::new_test_other_tenant();
        assert!(other_tenant_repo.get(user.id).await.is_none());
    }
}

Stage 4: Integration Validation

Agents validate interfaces between components:

Integration_Points:
  DATABASE_to_API:
    - Repository interfaces match handler expectations
    - Error types properly propagated
    - Transaction boundaries respected
    
  API_to_FRONTEND:
    - OpenAPI spec matches TypeScript types
    - WebSocket message formats aligned
    - Authentication tokens compatible

↑ Back to Top

4. Agent Specializations and Dependencies

Dependency Graph

Agent Capabilities

DATABASE_SPECIALIST

• FoundationDB client implementation
• Multi-tenant key schemas
• Repository pattern implementations
• Migration scripts
• Backup strategies

API_SPECIALIST

• Actix-web server configuration
• REST endpoint implementation
• JWT authentication middleware
• Request validation
• OpenAPI documentation

WEBSOCKET_SPECIALIST

• Real-time gateway server
• Binary protocol handling
• Connection management
• Message routing
• PTY integration

FRONTEND_SPECIALIST

• React application structure
• State management setup
• API client generation
• Component library
• Routing configuration

↑ Back to Top

5. Code Generation and Testing

Test-Driven Implementation

Agents follow TDD principles from TEST-DRIVEN-DESIGN-STANDARD-v4:

// 1. Agent writes test first
#[test]
fn workspace_creation_requires_authentication() {
    let app = create_test_app();
    let response = app.post("/workspaces").send().await;
    assert_eq!(response.status(), 401);
}

// 2. Then implements to pass test
pub async fn create_workspace(auth: Auth, req: CreateworkspaceRequest) -> Result<workspace> {
    auth.require_authenticated()?;
    // Implementation details
}

Coverage Requirements

Each component must meet coverage thresholds:

Coverage_Requirements:
  Unit_Tests: 95%
  Integration_Tests: 80%
  End_to_End_Tests: Critical paths only
  
Enforcement:
  - Pre-commit hooks validate coverage
  - CI pipeline blocks merges below threshold
  - Agents regenerate tests if coverage drops

Performance Testing

#[bench]
fn bench_user_repository_create(b: &mut Bencher) {
    let runtime = tokio::runtime::Runtime::new().unwrap();
    let repo = runtime.block_on(create_test_repository());
    
    b.iter(|| {
        runtime.block_on(async {
            let user = User::new_test();
            repo.create(user).await.unwrap()
        })
    });
    
    // Assert performance requirements
    assert!(b.ns_per_iter() < 1_000_000); // <1ms
}

↑ Back to Top

6. Build and Container Strategy

Multi-Stage Docker Builds

CONTAINER_SPECIALIST creates optimized images:

# Build stage
FROM rust:1.75 AS builder
WORKDIR /app
COPY cargo.toml Cargo.lock ./
RUN mkdir src && echo "fn main() {}" > src/main.rs
RUN cargo build --release && rm -rf src target/release/deps/coditect*

COPY . .
RUN touch src/main.rs && cargo build --release

# Runtime stage
FROM gcr.io/distroless/cc-debian12
COPY --from=builder /app/target/release/coditect /app/
COPY --from=builder /app/config /app/config

EXPOSE 8080
ENTRYPOINT ["/app/coditect"]

Container Security

Security_Measures:
  Base_Images:
    - Distroless for minimal attack surface
    - Specific version pinning
    - Regular vulnerability scanning
    
  Build_Process:
    - No secrets in images
    - Non-root user execution
    - Read-only filesystems where possible
    
  Registry:
    - Vulnerability scanning on push
    - Image signing
    - Access control policies

Build Pipeline

↑ Back to Top

7. Deployment and Infrastructure

Infrastructure as Code

CLOUD_SPECIALIST generates Terraform configurations:

# Cloud Run service definition
resource "google_cloud_run_service" "api" {
  name     = "coditect-api"
  location = "us-central1"
  
  template {
    spec {
      service_account_name = google_service_account.api.email
      
      containers {
        image = "${var.registry}/api:${var.version}"
        
        resources {
          limits = {
            cpu    = "2000m"
            memory = "2Gi"
          }
        }
        
        startup_probe {
          http_get {
            path = "/health/startup"
          }
          initial_delay_seconds = 10
          period_seconds        = 5
          failure_threshold     = 30
        }
        
        liveness_probe {
          http_get {
            path = "/health/live"
          }
          period_seconds = 10
        }
      }
    }
    
    metadata {
      annotations = {
        "autoscaling.knative.dev/minScale" = "2"
        "autoscaling.knative.dev/maxScale" = "100"
      }
    }
  }
}

Deployment Strategy

Deployment_Configuration:
  Strategy: blue_green
  
  Stages:
    - name: validate
      steps:
        - terraform plan
        - security scan
        - cost analysis
        
    - name: canary
      steps:
        - deploy 1% traffic
        - monitor metrics for 10m
        - automated rollback on errors
        
    - name: progressive
      steps:
        - increase to 10% (10m)
        - increase to 50% (10m)
        - increase to 100% (5m)
        
    - name: verification
      steps:
        - end-to-end tests
        - performance validation
        - security verification

Service Mesh Configuration

apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: coditect-api
spec:
  http:
  - match:
    - headers:
        canary:
          exact: "true"
    route:
    - destination:
        host: coditect-api
        subset: v2
      weight: 100
  - route:
    - destination:
        host: coditect-api
        subset: v1
      weight: 90
    - destination:
        host: coditect-api
        subset: v2
      weight: 10

↑ Back to Top

8. Monitoring and Operations

Observability Stack

MONITORING_SPECIALIST implements comprehensive monitoring:

Metrics_Collection:
  Application:
    - Request rate, latency, errors (RED metrics)
    - Business metrics (users, workspaces, executions)
    - Resource utilization (CPU, memory, connections)
    
  Infrastructure:
    - Node health
    - Network performance
    - Disk I/O
    - Container metrics
    
  Custom:
    - AI agent performance
    - Code generation success rate
    - Deployment frequency

Alert Configuration

alerts:
  - name: high_error_rate
    expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.05
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"
      
  - name: latency_spike
    expr: histogram_quantile(0.99, http_request_duration_seconds) > 0.5
    for: 10m
    labels:
      severity: warning
      
  - name: pod_restart_loop
    expr: rate(kube_pod_container_status_restarts_total[15m]) > 0.1
    labels:
      severity: critical

Operational Runbooks

Each alert includes automated remediation:

class RemediationController:
    def handle_alert(self, alert):
        match alert.name:
            case "high_error_rate":
                self.initiate_rollback()
                self.page_on_call()
                
            case "latency_spike":
                self.scale_up_instances()
                self.enable_cache_warming()
                
            case "pod_restart_loop":
                self.cordon_node()
                self.reschedule_pods()

Performance Dashboards

Conclusion

ADR-027 provides a comprehensive framework for coordinating multiple AI agents to transform specifications into deployed production systems. Through careful orchestration, dependency management, and automated quality gates, the framework ensures reliable and repeatable deployments.

---

Related ADRs

• ADR-001: Container Execution Architecture
• ADR-014: Deployment Pipeline
• ADR-016: CODI Command Interface
• ADR-024: Security Hardening Architecture

References

• Kubernetes Operators Pattern
• Google SRE Practices
• The Twelve-Factor App
• Continuous Delivery Pipelines

Approval Signatures

Role	Name	Date	Signature
Author	SESSION8-ORCHESTRATOR	2025-09-01	✓
Technical Review	Pending	-	-
QA Review	SESSION10-QA-REVIEWER2	Pending	-
Final Approval	Pending	-	-

Version History

Version	Date	Author	Changes
1.0.0	2025-09-01	SESSION8-ORCHESTRATOR	Technical implementation focus without financial content