Implementation Roadmap: Agentic SDLC Orchestration

Generated: 2026-02-06
Source: Research corpus analysis of 6 topic areas
Status: Draft for review

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1. Executive Summary

Based on analysis of the research corpus covering Agentic Loops, Feedback Mechanisms, Code Review, Testing/QA, CI/CD, and SDLC Orchestration, this roadmap proposes a phased implementation approach for building an AI-driven SDLC orchestration system.

Key Finding: Start with Feedback Loops as the foundational layer, then build Code Review as the first vertical slice, followed by Testing and CI/CD integration. The full SDLC orchestrator comes last once primitives are proven.

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2. Priority Matrix

Quick Wins (Week 1-2)

1. Structured feedback capture - Store errors, patterns, resolutions
2. Basic code review bot - PR comments, linting integration
3. Test failure analyzer - Parse Jest/Vitest output, suggest fixes

Long-Term Investments (Week 6-8+)

1. Multi-agent orchestration - Parallel implementation agents
2. Self-improving memory - GEP protocol implementation
3. Autonomous deployment - Canary with AI health analysis

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3. Critical Path Dependencies

┌─────────────────────────────────────────────────────────────────────────────┐
│                          DEPENDENCY GRAPH                                    │
└─────────────────────────────────────────────────────────────────────────────┘

Phase 1: Foundation (Weeks 1-2)
─────────────────────────────────
┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   Memory     │────▶│  Feedback    │────▶│  Reflection  │
│   Store      │     │  Capture     │     │  Engine      │
└──────────────┘     └──────────────┘     └──────┬───────┘
                                                  │
                                                  ▼
┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   Error      │────▶│   Pattern    │────▶│   Gene/      │
│  Taxonomy    │     │  Matching    │     │  Capsule DB  │
└──────────────┘     └──────────────┘     └──────────────┘

Phase 2: Vertical Slices (Weeks 3-5)
─────────────────────────────────────
                                                  ┌──────────────┐
┌──────────────┐     ┌──────────────┐     ┌─────▶│  Code Review │
│  Feedback    │────▶│  Tool Use    │─────┘      │    Agent     │
│  System      │     │  Framework   │            └──────────────┘
└──────────────┘     └──────────────┘            
       │                                          ┌──────────────┐
       │                                    ┌─────▶│ Test Runner  │
       │                                    │      │  Integration │
       ▼                                    │      └──────────────┘
┌──────────────┐     ┌──────────────┐     ┌┴─────────────┐
│   GitHub     │────▶│    Git       │────▶│  CI/CD Hook  │
│    API       │     │   Parser     │     │   Handler    │
└──────────────┘     └──────────────┘     └──────────────┘

Phase 3: Orchestration (Weeks 6-8)
───────────────────────────────────
┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│ Code Review  │────▶│   SDLC       │◀────│    Test      │
│    Agent     │     │ Orchestrator │     │    Agent     │
└──────────────┘     └──────┬───────┘     └──────────────┘
                            │
              ┌─────────────┼─────────────┐
              ▼             ▼             ▼
        ┌──────────┐  ┌──────────┐  ┌──────────┐
        │ Planner  │  │Deployer  │  │ Monitor  │
        │  Agent   │  │  Agent   │  │  Agent   │
        └──────────┘  └──────────┘  └──────────┘

Hard Dependencies (Must be built first)

Soft Dependencies (Can be stubbed initially)

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4. Phase Breakdown (8 Weeks)

Phase 1: Foundation Layer (Weeks 1-2)

Goal: Build the primitives that everything else depends on

Week 1: Memory & Feedback Infrastructure

typescript
// Core interfaces to implement
interface MemoryStore {
  store(event: LearningEvent): Promise<void>;
  query(context: Context): Promise<Pattern[]>;
  consolidate(): Promise<void>;
}

interface FeedbackCapture {
  captureError(error: Error, context: Context): Promise<void>;
  captureSuccess(execution: Execution): Promise<void>;
  captureReview(feedback: ReviewComment): Promise<void>;
}

Deliverables:

• SQLite-based memory store schema
• Error taxonomy with 5 categories: syntax, runtime, logic, api, system
• Event capture pipeline (structured logging)
• Basic pattern matching (string similarity)

Week 2: Reflection Engine

typescript
interface ReflectionEngine {
  reflect(execution: Execution): Promise<Insights>;
  suggestImprovements(pattern: Pattern): Promise<Suggestion[]>;
  validateLearning(suggestion: Suggestion): Promise<boolean>;
}

Deliverables:

• Post-execution reflection logic
• Confidence scoring for suggestions
• Gene/Capsule data model (GEP protocol subset)
• Human approval gate for high-impact learnings

Risk Mitigation:

• Start with JSON file storage, migrate to SQLite later
• Hard confidence thresholds (0.8+) before auto-applying
• All learnings logged for audit

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Phase 2: First Vertical Slice - Code Review (Weeks 3-4)

Goal: Prove value with a complete, usable agent

Week 3: Core Review Agent

typescript
interface CodeReviewAgent {
  parsePR(pr: PR): Promise<CodebaseDiff>;
  runStaticAnalysis(diff: Diff): Promise<Issue[]>;
  aiReview(diff: Diff, context: Context): Promise<Comment[]>;
  generateReport(results: Results): Promise<ReviewReport>;
}

Deliverables:

• GitHub PR webhook handler
• Diff parser (TypeScript/JavaScript focus)
• Integration with eslint/prettier
• LLM review prompts with examples
• Risk-based review depth (low/medium/high)

Week 4: GitHub Integration & Feedback Loop

typescript
interface GitHubIntegration {
  postReview(pr: PR, review: Review): Promise<void>;
  updateCheckStatus(pr: PR, status: Status): Promise<void>;
  learnFromDismissals(pr: PR): Promise<void>;
}

Deliverables:

• PR comment posting
• Check status integration
• Dismissal tracking (learn what humans disagree with)
• Quality dashboard (metrics display)

Success Criteria:

• Agent reviews PRs within 60 seconds
• False positive rate < 30% (learn from dismissals)
• Captures feedback for continuous improvement

Risk Mitigation:

• Start with low-risk repos only
• Human approval required for "blocking" comments
• Easy disable switch per repo

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Phase 3: Testing Integration (Week 5)

Goal: Augment testing with AI capabilities

typescript
interface TestingAgent {
  selectTests(changes: Diff, allTests: Test[]): Promise<Test[]>;
  analyzeFailure(failure: TestFailure): Promise<Diagnosis>;
  generateTest(code: Function, spec: Spec): Promise<Test>;
}

Deliverables:

• Risk-based test selector (change impact analysis)
• Failure analyzer with root cause suggestions
• Test gap identification (untested changes)
• Integration with Jest/Vitest

Quick Win:

typescript
// Immediate value: smarter test failure messages
const diagnosis = await analyzeFailure(testFailure);
// Output: "This test fails because the mock wasn't reset between tests. 
//          Add beforeEach(() => jest.clearAllMocks())"

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Phase 4: CI/CD Pipeline (Week 6)

Goal: Connect agents to deployment pipeline

typescript
interface CICDIntegration {
  onBuildComplete(build: Build): Promise<void>;
  onTestComplete(results: TestResults): Promise<void>;
  onDeployRequest(deploy: DeployConfig): Promise<Decision>;
}

Deliverables:

• GitHub Actions integration
• Build failure analysis
• Pre-deploy risk assessment
• Basic deployment check (synthetic health endpoint)

Scope Limitation:

• No auto-rollback in MVP (alert only)
• Manual approval for production deploys
• Focus on analysis/reporting, not automation

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Phase 5: SDLC Orchestrator MVP (Weeks 7-8)

Goal: Coordinate multiple agents for end-to-end flow

typescript
interface SDLCOrchestrator {
  // MVP: Sequential execution only
  async execute(requirements: string): Promise<void> {
    const design = await planner.createDesign(requirements);
    const code = await implementer.implement(design);
    const reviewed = await reviewer.review(code);
    const tested = await tester.test(reviewed);
    await deployer.deploy(tested); // with human approval
  }
}

Deliverables:

• Shared context bus (in-memory MVP)
• Checkpoint system (save/restore state)
• Sequential orchestration flow
• Human approval gates
• Basic telemetry/metrics

Explicitly NOT Included (Post-MVP):

• Parallel agent execution
• Autonomous deployment
• Self-directed planning
• Multi-repo coordination

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5. Risk Assessment by Component

Component: Memory & Feedback

Component: Code Review Agent

Component: Testing Agent

Component: CI/CD Integration

Component: SDLC Orchestrator

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6. Risk Mitigation Strategies

Strategy 1: Gradual Automation Ladder

Level 0: Human only (current state)
    ↓
Level 1: AI suggests, human decides (Weeks 3-4)
    ↓
Level 2: AI acts, human reviews (Weeks 5-6)
    ↓
Level 3: AI acts, human notified (Weeks 7-8)
    ↓
Level 4: Full autonomy (Post-MVP)

Never skip levels. Each level requires metrics proving safety.

Strategy 2: Circuit Breakers (Everywhere)

typescript
interface SafetyControls {
  maxIterations: 10;
  maxCostPerTask: 5.00; // USD
  maxTimePerTask: 300000; // 5 minutes
  requireHumanApproval: [
    'productionDeploy',
    'securityChanges',
    'breakingChanges'
  ];
  autoRollbackTriggers: {
    errorRate: 0.01;
    latency: 1.5; // 1.5x baseline
  };
}

Strategy 3: Observable Everything

• Every decision logged with rationale
• Every action attributed to an agent
• Cost tracking per execution
• Quality metrics trended over time

Strategy 4: Human-in-the-Loop Defaults

typescript
// Default configuration requires human approval
const defaultConfig = {
  autoFix: false,           // Suggest only
  autoDeploy: false,        // Require approval
  autoMerge: false,         // Human decides
  learningThreshold: 0.9,   // High confidence required
};

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7. Success Metrics

Technical Metrics

Business Metrics

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8. Post-MVP Roadmap (Months 3-6)

Q2 Priorities

1. Multi-Agent Parallelization - Multiple implementer agents working on different modules
2. Advanced Memory - Knowledge graphs, cross-session learning
3. Self-Healing - Auto-fix common issues without human intervention
4. Cross-Repo Intelligence - Learn patterns across multiple codebases

Q3+ Vision

1. Autonomous Planning - Agents break down requirements without human help
2. Natural Language Requirements - Stakeholders describe features in plain English
3. Continuous Self-Improvement - System improves its own prompts and strategies
4. Ecosystem Integration - Jira, Slack, Notion, Figma integration

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9. Appendix: Implementation Checklist

Week 1

• SQLite schema for memory store
• Error taxonomy definition
• Event capture pipeline
• Basic pattern matching

Week 2

• Reflection engine
• Gene/Capsule data model
• Confidence scoring
• Human approval workflow

Week 3

• PR webhook handler
• Diff parser
• Static analysis integration
• LLM review prompts

Week 4

• GitHub comment posting
• Check status integration
• Dismissal tracking
• Quality dashboard

Week 5

• Test selector
• Failure analyzer
• Test gap detection
• Jest/Vitest integration

Week 6

• GitHub Actions integration
• Build failure analysis
• Pre-deploy risk assessment
• Synthetic health checks

Week 7

• Shared context bus
• Checkpoint system
• Sequential orchestration
• Approval gates

Week 8

• End-to-end flow testing
• Telemetry/metrics
• Documentation
• Demo preparation

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10. Decision Log

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End of Roadmap Document