architecture

February 8, 2026

End-to-End SDLC Orchestration

title: "End-to-End SDLC Orchestration" description: "Research on orchestrating the complete software development lifecycle with AI agents" date: 2026-02-06 topics: [sdlc, orchestration, workflows, lifecycle] sources: 0 status: initial

End-to-End SDLC Orchestration

Overview

This research synthesizes findings from previous topics into a comprehensive framework for orchestrating the entire software development lifecycle using AI agents. The goal is a system where human input is minimized to requirements and high-level direction, while agents handle implementation, validation, and deployment.

The Ideal SDLC Flow

Requirements (Human)
       │
       ▼
┌─────────────┐
│   Planning  │──▶ Agent analyzes requirements
│   & Design  │    creates architecture
└──────┬──────┘
       │
       ▼
┌─────────────┐
│Implementation│──▶ Agents write code in parallel
└──────┬──────┘
       │
       ▼
┌─────────────┐
│    Review   │──▶ Automated + human review
└──────┬──────┘
       │
       ▼
┌─────────────┐
│   Testing   │──▶ Automated test execution
└──────┬──────┘
       │
       ▼
┌─────────────┐
│  Deployment │──▶ Automated CI/CD pipeline
└──────┬──────┘
       │
       ▼
┌─────────────┐
│  Monitoring │──▶ Continuous feedback
└──────┬──────┘
       │
       ▼
   Learn & Iterate

Orchestration Architecture

Multi-Agent System Design

typescript
interface SDLCOrchestrator {
  // Core orchestrator manages the lifecycle
  
  agents: {
    planner: PlanningAgent;           // Requirements → Architecture
    implementer: ImplementationAgent; // Architecture → Code
    reviewer: ReviewAgent;            // Code → Validated Code
    tester: TestingAgent;             // Code → Tested Code
    deployer: DeploymentAgent;        // Code → Deployed Service
    monitor: MonitoringAgent;         // Runtime → Insights
  };
  
  async executeLifecycle(
    requirements: Requirements
  ): Promise<DeployedSystem> {
    // Phase 1: Planning
    const design = await this.agents.planner.createDesign(requirements);
    await this.checkpoint('planning_complete', design);
    
    // Phase 2: Implementation
    const code = await this.agents.implementer.implement(design);
    await this.checkpoint('implementation_complete', code);
    
    // Phase 3: Review
    const reviewedCode = await this.agents.reviewer.review(code);
    if (!reviewedCode.approved) {
      return this.requestChanges(reviewedCode.feedback);
    }
    
    // Phase 4: Testing
    const testResults = await this.agents.tester.test(reviewedCode);
    if (!testResults.passed) {
      return this.fixAndRetry(testResults.failures);
    }
    
    // Phase 5: Deployment
    const deployment = await this.agents.deployer.deploy(testResults.artifact);
    
    // Phase 6: Monitoring
    await this.agents.monitor.startMonitoring(deployment);
    
    return deployment;
  }
}

Agent Communication Patterns

Shared Context Bus

typescript
interface ContextBus {
  // All agents share context
  requirements: Requirements;
  design: ArchitectureDesign;
  codebase: CodeRepository;
  testResults: TestResults;
  deployment: DeploymentInfo;
  
  // Event streaming
  events: EventStream;
  
  // Knowledge base
  learnings: Learning[];
  patterns: Pattern[];
}

Message Passing

typescript
interface AgentMessage {
  from: AgentId;
  to: AgentId | 'broadcast';
  type: 'request' | 'response' | 'notification';
  payload: unknown;
  context: ContextRef;
  priority: number;
}

Phase-by-Phase Breakdown

1. Requirements & Planning Phase

Human Input:

High-level requirements
Constraints and non-functional requirements
Acceptance criteria

Agent Tasks:

typescript
interface PlanningAgent {
  async analyzeRequirements(reqs: Requirements): Promise<Analysis> {
    // Break down epics into stories
    const stories = await this.decompose(reqs);
    
    // Estimate complexity
    const estimates = await this.estimate(stories);
    
    // Identify dependencies
    const dependencies = await this.analyzeDependencies(stories);
    
    // Suggest architecture
    const architecture = await this.designArchitecture(reqs);
    
    return { stories, estimates, dependencies, architecture };
  }
}

Output:

Architecture document
Implementation plan
Risk assessment
Resource estimates

2. Implementation Phase

Agent Tasks:

typescript
interface ImplementationAgent {
  async implement(design: ArchitectureDesign): Promise<Codebase> {
    // Create implementation plan
    const plan = await this.createPlan(design);
    
    // Spawn worker agents for parallel development
    const workers = await this.spawnWorkers(plan.modules);
    
    // Coordinate development
    const modules = await Promise.all(
      workers.map(w => w.implement())
    );
    
    // Integration
    const codebase = await this.integrate(modules);
    
    // Self-validation
    const validation = await this.validate(codebase);
    if (!validation.passed) {
      await this.fixIssues(validation.issues);
    }
    
    return codebase;
  }
}

Patterns:

Swarm development (parallel implementation)
Test-driven generation
Incremental refinement
Continuous validation

3. Review Phase

Agent Tasks:

typescript
interface ReviewAgent {
  async review(code: Codebase): Promise<ReviewResult> {
    // Automated checks
    const staticAnalysis = await this.runStaticAnalysis(code);
    const securityScan = await this.runSecurityScan(code);
    const coverage = await this.checkTestCoverage(code);
    
    // AI review
    const aiReview = await this.aiCodeReview(code);
    
    // Risk assessment
    const risk = this.assessRisk(code, staticAnalysis, aiReview);
    
    // Determine review depth
    if (risk.score > HIGH_THRESHOLD) {
      // Require human review
      return {
        approved: false,
        requiresHuman: true,
        feedback: this.compileFeedback(staticAnalysis, aiReview)
      };
    }
    
    return { approved: true, issues: aiReview.issues };
  }
}

Decision Matrix:

Risk Level	Automated	Human Required
Low	Auto-approve	No
Medium	Suggest fixes	Optional
High	Block	Yes

4. Testing Phase

Agent Tasks:

typescript
interface TestingAgent {
  async test(code: Codebase): Promise<TestResults> {
    // Generate tests if needed
    const generatedTests = await this.generateTests(code);
    
    // Select tests based on changes
    const selectedTests = await this.selectTests(code, generatedTests);
    
    // Execute tests
    const results = await this.executeTests(selectedTests);
    
    // Analyze failures
    if (results.failed.length > 0) {
      const analysis = await this.analyzeFailures(results.failed);
      
      // Auto-fix if possible
      const fixes = await this.suggestFixes(analysis);
      if (fixes.confidence > THRESHOLD) {
        await this.applyFixes(fixes);
        return this.retest(code);
      }
    }
    
    return results;
  }
}

5. Deployment Phase

Agent Tasks:

typescript
interface DeploymentAgent {
  async deploy(artifact: BuildArtifact): Promise<Deployment> {
    // Validate artifact
    const validation = await this.validateArtifact(artifact);
    
    // Determine deployment strategy
    const strategy = this.selectStrategy(validation);
    
    // Execute deployment
    const deployment = await this.executeDeployment(artifact, strategy);
    
    // Validate deployment
    const postDeploy = await this.validateDeployment(deployment);
    
    if (!postDeploy.healthy) {
      await this.rollback(deployment);
      throw new DeploymentError(postDeploy.issues);
    }
    
    return deployment;
  }
}

6. Monitoring Phase

Agent Tasks:

typescript
interface MonitoringAgent {
  async startMonitoring(deployment: Deployment): Promise<void> {
    // Set up metrics collection
    await this.configureMonitoring(deployment);
    
    // Start continuous validation
    this.startSyntheticTests(deployment);
    
    // Watch for anomalies
    this.watchMetrics(deployment, {
      errorRate: { threshold: 0.01 },
      latency: { threshold: 'p95 < 500ms' },
      throughput: { threshold: 'baseline - 10%' }
    });
    
    // Feedback loop
    this.onAnomaly(async (anomaly) => {
      const diagnosis = await this.diagnose(anomaly);
      await this.feedbackToOrchestrator(diagnosis);
    });
  }
}

Observability Across SDLC

Unified Telemetry

typescript
interface SDLCTelemetry {
  // Per-phase metrics
  planning: {
    duration: number;
    iterations: number;
    humanInterventions: number;
  };
  
  implementation: {
    linesOfCode: number;
    testCoverage: number;
    complexityScore: number;
    agentEfficiency: number;
  };
  
  review: {
    issuesFound: number;
    falsePositiveRate: number;
    humanReviewTime: number;
  };
  
  testing: {
    testsRun: number;
    passRate: number;
    flakiness: number;
    executionTime: number;
  };
  
  deployment: {
    leadTime: number;
    successRate: number;
    rollbackRate: number;
  };
  
  // Cross-cutting concerns
  cost: {
    compute: number;
    apiCalls: number;
    humanHours: number;
  };
  
  quality: {
    defectEscapeRate: number;
    technicalDebt: number;
    maintainability: number;
  };
}

Audit Trail

typescript
interface SDLCEvent {
  traceId: string;
  phase: Phase;
  timestamp: Date;
  agent: AgentId;
  action: string;
  input: unknown;
  output: unknown;
  decisions: Decision[];
  feedback: Feedback[];
}

Human Integration Points

Required Human Input

Requirements Definition
- Business objectives
- Constraints
- Acceptance criteria
Architecture Approval
- High-level design review
- Technology choices
- Trade-off decisions
Risk-Based Reviews
- High-risk changes
- Security-sensitive code
- Business-critical features
Final Approval
- Production deployment
- Major releases

Optional Human Input

Code review feedback
Test case suggestions
Design refinements
Priority adjustments

Reliability and Safety

Circuit Breakers

typescript
interface SafetyControls {
  // Prevent runaway loops
  maxIterations: number;
  maxCost: number;
  maxTime: number;
  
  // Require human approval for
  productionDeploy: boolean;
  securityChanges: boolean;
  breakingChanges: boolean;
  
  // Auto-rollback triggers
  errorRateThreshold: number;
  latencyThreshold: number;
}

Checkpoint System

typescript
interface CheckpointSystem {
  async checkpoint(phase: Phase, state: State): Promise<void> {
    // Save state
    await this.persistState(phase, state);
    
    // Validate checkpoint
    const validation = await this.validateState(state);
    
    // Human review if needed
    if (validation.requiresReview) {
      await this.requestHumanReview(phase, state);
    }
    
    // Enable rollback
    this.enableRollback(phase);
  }
}

Research Summary and Gaps

What We Know

Individual phases can be automated with AI
Multi-agent systems can parallelize work
Feedback loops enable continuous improvement
Human-in-the-loop is essential for high-stakes decisions

What We Don't Know

Optimal agent granularity (many small vs few large)
Cost-effectiveness vs traditional development
Handling of novel/ambiguous requirements
Long-term maintainability of agent-generated code
Legal/IP implications of AI-generated code
Optimal human-AI collaboration patterns

Next Steps

Prototype minimal viable orchestrator
Measure against baseline (human-only development)
Iterate on agent definitions
Build shared context infrastructure
Define safety constraints