Agentic Quality Engineering Fleet

V3 (Main) | V2 Documentation | Changelog | Contributors | Issues | Discussions

V3 brings Domain-Driven Design architecture, 12 bounded contexts, 50 specialized QE agents, TinyDancer intelligent model routing, ReasoningBank learning with Dream cycles, HNSW vector search, and deep integration with Claude Flow and Agentic Flow.

🏗️ DDD Architecture | 🧠 ReasoningBank + Dream Cycles | 🎯 TinyDancer Model Routing | 🔍 HNSW Vector Search | 👑 Queen Coordinator | 📊 O(log n) Coverage | 🔗 Claude Flow Integration | 🎯 12 Bounded Contexts | 📚 60 QE Skills

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⚡ Quick Start

Install & Initialize

# Install globally
npm install -g agentic-qe

# Initialize your project
cd your-project
aqe init --wizard

# Or with auto-configuration
aqe init --auto

# Add MCP server to Claude Code (optional)
claude mcp add agentic-qe npx agentic-qe mcp

# Verify connection
claude mcp list

Use from Claude Code CLI

Ask Claude to use QE agents directly from your terminal:

# Generate comprehensive tests with learning
claude "Use qe-test-architect to create tests for src/services/user-service.ts with 95% coverage"

# Run full quality pipeline with Queen coordination
claude "Use qe-queen-coordinator to orchestrate: test generation, coverage analysis, security scan, and quality gate"

# Detect flaky tests with root cause analysis
claude "Use qe-flaky-hunter to analyze the last 100 test runs and stabilize flaky tests"

What V3 provides:

• ✅ 12 DDD Bounded Contexts: Organized by business domain (test-generation, coverage-analysis, security-compliance, etc.)
• ✅ 50 QE Agents: Including Queen Coordinator for hierarchical orchestration (43 main + 7 TDD subagents)
• ✅ TinyDancer Model Routing: 3-tier intelligent routing (Haiku/Sonnet/Opus) for cost optimization
• ✅ ReasoningBank Learning: HNSW-indexed pattern storage with experience replay
• ✅ O(log n) Coverage Analysis: Sublinear algorithms for efficient gap detection
• ✅ Claude Flow Integration: Deep integration with MCP tools and swarm orchestration
• ✅ Memory Coordination: Cross-agent communication via aqe/v3/* namespaces
• ✅ V2 Backward Compatibility: All V2 agents map to V3 equivalents
• ✅ 60 QE Skills: Domain-specific skills for testing, security, accessibility, and more

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🎯 Why AQE?

Problem	AQE Solution
Writing comprehensive tests is tedious and time-consuming	AI agents generate tests automatically with pattern reuse across projects
Test suites become slow and expensive at scale	Sublinear O(log n) algorithms for coverage analysis and intelligent test selection
Flaky tests waste developer time debugging false failures	ML-powered detection with root cause analysis and fix recommendations
AI testing tools are expensive	TinyDancer 3-tier model routing reduces costs by matching task complexity to appropriate model
No memory between test runs—every analysis starts from scratch	ReasoningBank remembers patterns, strategies, and what works for your codebase
Agents waste tokens reading irrelevant code	Code Intelligence provides token reduction with semantic search and knowledge graphs
Quality engineering requires complex coordination	Queen Coordinator orchestrates 50 agents across 12 domains with consensus and MinCut topology
Tools don't understand your testing frameworks	Works with Jest, Cypress, Playwright, Vitest, Mocha, Jasmine, AVA

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✨ V3 Features

🏗️ Domain-Driven Design Architecture

V3 is built on 12 DDD Bounded Contexts, each with dedicated agents and clear responsibilities:

Domain	Purpose	Key Agents
test-generation	AI-powered test creation	qe-test-architect, qe-tdd-specialist
test-execution	Parallel execution & retry	qe-parallel-executor, qe-retry-handler
coverage-analysis	O(log n) gap detection	qe-coverage-specialist, qe-gap-detector
quality-assessment	Quality gates & decisions	qe-quality-gate, qe-risk-assessor
defect-intelligence	Prediction & root cause	qe-defect-predictor, qe-root-cause-analyzer
requirements-validation	BDD & testability	qe-requirements-validator, qe-bdd-generator
code-intelligence	Knowledge graph & search	qe-code-intelligence, qe-kg-builder
security-compliance	SAST/DAST & audit	qe-security-scanner, qe-security-auditor
contract-testing	API contracts & GraphQL	qe-contract-validator, qe-graphql-tester
visual-accessibility	Visual regression & a11y	qe-visual-tester, qe-accessibility-auditor
chaos-resilience	Chaos engineering & load	qe-chaos-engineer, qe-load-tester
learning-optimization	Cross-domain learning	qe-learning-coordinator, qe-pattern-learner

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👑 Queen Coordinator & Hierarchical Orchestration

The qe-queen-coordinator manages the entire fleet with intelligent task distribution:

                    qe-queen-coordinator
                           (Queen)
                             |
        +--------------------+--------------------+
        |                    |                    |
   TEST DOMAIN          QUALITY DOMAIN       LEARNING DOMAIN
   (test-generation)    (quality-assessment) (learning-optimization)
        |                    |                    |
   - test-architect     - quality-gate       - learning-coordinator
   - tdd-specialist     - risk-assessor      - pattern-learner
   - integration-tester - deployment-advisor - transfer-specialist

Capabilities:

• Orchestrate 50 QE agents concurrently across 12 domains
• TinyDancer 3-tier model routing (Haiku/Sonnet/Opus) with confidence-based decisions
• Byzantine fault-tolerant consensus for critical quality decisions
• MinCut graph-based topology optimization for self-healing coordination
• Memory-backed cross-agent communication with HNSW vector search
• Work stealing with adaptive load balancing (3-5x throughput improvement)

claude "Use qe-queen-coordinator to orchestrate release validation for v2.1.0 with 90% coverage target"

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🧠 ReasoningBank Learning System

V3 agents learn and improve through the ReasoningBank pattern storage:

Component	Description
Experience Storage	Store successful patterns with confidence scores
HNSW Indexing	Fast O(log n) similarity search for pattern matching
Experience Replay	Learn from past successes and failures
Cross-Project Transfer	Share patterns between projects

# Check what agents have learned
aqe memory search --query "test patterns" --namespace learning

# View learning metrics
aqe hooks metrics --v3-dashboard

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🌙 Dream Cycles & Neural Learning

V3 introduces Dream cycles for neural consolidation and continuous improvement:

Feature	Description
Dream Cycles	Background neural consolidation (30s max) with spreading activation
9 RL Algorithms	Q-Learning, SARSA, DQN, PPO, A2C, DDPG, Actor-Critic, Policy Gradient, Decision Transformer
SONA Integration	Self-Optimizing Neural Architecture with <0.05ms adaptation
Novelty Scoring	Prioritize learning from novel patterns
Concept Graphs	Build semantic connections between quality patterns

# Trigger dream cycle for pattern consolidation
aqe hooks intelligence --mode dream --consolidate

# View learning trajectory
aqe hooks intelligence trajectory-start --task "optimize coverage"

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🎯 TinyDancer Intelligent Model Routing

TinyDancer (ADR-026) provides 3-tier intelligent model routing for cost optimization:

Complexity Score	Model	Use Cases
0-20 (Simple)	Haiku	Syntax fixes, type additions, simple refactors
20-70 (Moderate)	Sonnet	Bug fixes, test generation, code review
70+ (Critical)	Opus	Architecture, security, complex reasoning

Routing Features:

• Confidence-based decisions: Routes based on task complexity analysis
• Automatic escalation: Escalates to higher-tier model if confidence is low
• Learning from outcomes: Improves routing based on success/failure patterns
• Token budget optimization: Minimizes cost while maintaining quality

# Check model routing for a task
aqe hooks model-route --task "fix type errors in user-service.ts"

# View routing statistics
aqe hooks model-stats

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🔐 Consensus & MinCut Coordination

V3 includes advanced coordination mechanisms for reliable multi-agent decisions:

Feature	Description
Byzantine Consensus	Fault-tolerant voting for critical quality decisions
MinCut Topology	Graph-based self-healing agent coordination
Multi-Model Voting	Aggregate decisions from multiple model tiers
Claim Verification	Cryptographic verification of agent work claims

# View consensus status
aqe coordination consensus --status

# Check topology health
aqe coordination topology --optimize

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🔍 O(log n) Coverage Analysis

Efficient coverage gap detection using Johnson-Lindenstrauss algorithms:

• Sublinear complexity: Analyze large codebases in logarithmic time
• Risk-weighted gaps: Prioritize coverage by business impact
• Intelligent test selection: Minimal tests for maximum coverage
• Trend tracking: Monitor coverage changes over time

claude "Use qe-coverage-specialist to analyze gaps in src/ with risk scoring"

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🔗 Claude Flow Integration

V3 deeply integrates with Claude Flow for:

• MCP Server: All V3 tools available via Model Context Protocol
• Swarm Orchestration: Multi-agent coordination with hierarchical topology
• Memory Sharing: Cross-agent state via aqe/v3/* namespaces
• Hooks System: Pre/post task learning and optimization
• Session Management: Persistent state across conversations

# Initialize swarm with Claude Flow
npx @claude-flow/cli@latest swarm init --topology hierarchical-mesh

# Spawn V3 agents
npx @claude-flow/cli@latest agent spawn -t qe-test-architect --name test-gen

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📊 50 Specialized QE Agents

Category	Count	Highlights
Main QE Agents	43	Test generation, coverage, security, performance, accessibility
TDD Subagents	7	RED/GREEN/REFACTOR with code review

V2 Backward Compatibility: All V2 agents map to V3 equivalents automatically.

📋 View All Main QE Agents (43)

Agent	Domain	Purpose
qe-queen-coordinator	coordination	Hierarchical fleet orchestration
qe-test-architect	test-generation	AI-powered test creation
qe-tdd-specialist	test-generation	TDD workflow coordination
qe-parallel-executor	test-execution	Multi-worker test execution
qe-retry-handler	test-execution	Intelligent retry with backoff
qe-coverage-specialist	coverage-analysis	O(log n) coverage analysis
qe-gap-detector	coverage-analysis	Risk-weighted gap detection
qe-quality-gate	quality-assessment	Quality threshold validation
qe-risk-assessor	quality-assessment	Multi-factor risk scoring
qe-deployment-advisor	quality-assessment	Go/no-go deployment decisions
qe-defect-predictor	defect-intelligence	ML-powered defect prediction
qe-root-cause-analyzer	defect-intelligence	Systematic root cause analysis
qe-flaky-hunter	defect-intelligence	Flaky test detection & fix
qe-requirements-validator	requirements-validation	Testability analysis
qe-bdd-generator	requirements-validation	Gherkin scenario generation
qe-code-intelligence	code-intelligence	Semantic code search
qe-kg-builder	code-intelligence	Knowledge graph construction
qe-dependency-mapper	code-intelligence	Dependency analysis
qe-security-scanner	security-compliance	SAST/DAST scanning
qe-security-auditor	security-compliance	Security audit & compliance
qe-contract-validator	contract-testing	API contract validation
qe-graphql-tester	contract-testing	GraphQL testing
qe-visual-tester	visual-accessibility	Visual regression testing
qe-accessibility-auditor	visual-accessibility	WCAG compliance testing
qe-responsive-tester	visual-accessibility	Cross-viewport testing
qe-chaos-engineer	chaos-resilience	Controlled fault injection
qe-load-tester	chaos-resilience	Load & performance testing
qe-performance-tester	chaos-resilience	Performance validation
qe-learning-coordinator	learning-optimization	Fleet-wide learning
qe-pattern-learner	learning-optimization	Pattern discovery
qe-transfer-specialist	learning-optimization	Cross-project transfer
qe-metrics-optimizer	learning-optimization	Hyperparameter tuning
qe-integration-tester	test-execution	Component integration
qe-mutation-tester	test-generation	Test effectiveness validation
qe-property-tester	test-generation	Property-based testing
qe-regression-analyzer	defect-intelligence	Regression risk analysis
qe-impact-analyzer	code-intelligence	Change impact assessment
qe-code-complexity	code-intelligence	Complexity metrics
qe-qx-partner	quality-assessment	QA + UX collaboration
qe-fleet-commander	coordination	Large-scale orchestration
qe-integration-architect	code-intelligence	V3 integration design
qe-product-factors-assessor	quality-assessment	SFDIPOT product factors analysis
qe-test-idea-rewriter	test-generation	Transform passive tests to active actions

🔧 TDD Subagents (7)

Subagent	Phase	Purpose
qe-tdd-red	RED	Write failing tests
qe-tdd-green	GREEN	Implement minimal code
qe-tdd-refactor	REFACTOR	Improve code quality
qe-code-reviewer	REVIEW	Code quality validation
qe-integration-reviewer	REVIEW	Integration review
qe-performance-reviewer	REVIEW	Performance review
qe-security-reviewer	REVIEW	Security review

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💻 V3 Usage Examples

Example 1: Queen-Coordinated Quality Pipeline

claude "Use qe-queen-coordinator to run full quality assessment:
1. Generate tests for src/services/*.ts
2. Execute tests with parallel workers
3. Analyze coverage gaps with risk scoring
4. Run security scan
5. Validate quality gate at 90% threshold
6. Provide deployment recommendation"

What happens:

1. Queen spawns domain coordinators for each task
2. Agents execute in parallel across 5 domains
3. Results aggregate through memory coordination
4. Queen synthesizes final recommendation

Example 2: Learning-Enhanced Test Generation

claude "Use qe-test-architect to create tests for PaymentService with:
- Property-based testing for validation
- 95% coverage target
- Apply learned patterns from similar services"

Output includes:

Generated 48 tests across 4 files
- unit/PaymentService.test.ts (32 unit tests)
- property/PaymentValidation.property.test.ts (8 property tests)
- integration/PaymentFlow.integration.test.ts (8 integration tests)
Coverage: 96.2%
Pattern reuse: 78% from learned patterns
Learning stored: "payment-validation-patterns" (confidence: 0.94)

Example 3: TDD Workflow with Subagents

claude "Use qe-tdd-specialist to implement UserAuthentication with full RED-GREEN-REFACTOR cycle"

Workflow:

1. qe-tdd-red: Writes failing tests defining behavior
2. qe-tdd-green: Implements minimal code to pass
3. qe-tdd-refactor: Improves code quality
4. qe-code-reviewer: Validates standards
5. qe-security-reviewer: Checks security concerns

Example 4: Cross-Domain Coordination

claude "Coordinate security audit across the monorepo:
- qe-security-scanner for SAST/DAST
- qe-dependency-mapper for vulnerability scanning
- qe-contract-validator for API security
- qe-chaos-engineer for resilience testing"

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🎓 60 QE Skills

V3 agents automatically apply relevant skills from the comprehensive skill library:

View All 60 QE Skills

Core Testing & Methodologies (12)

• agentic-quality-engineering - Core PACT principles for AI-powered QE
• holistic-testing-pact - Evolved testing model with PACT integration
• context-driven-testing - Practices chosen based on project context
• tdd-london-chicago - Test-driven development with both school approaches
• xp-practices - Extreme programming practices for quality
• risk-based-testing - Focus testing effort on highest-risk areas
• test-automation-strategy - Strategic approach to automation
• refactoring-patterns - Safe code improvement patterns
• shift-left-testing - Early testing in development lifecycle
• shift-right-testing - Production testing and observability
• regression-testing - Strategic regression management
• verification-quality - Quality verification practices

Specialized Testing (12)

• accessibility-testing - WCAG 2.2 compliance and inclusive design
• mobile-testing - iOS and Android platform testing
• database-testing - Schema validation and data integrity
• contract-testing - Consumer-driven contract testing
• chaos-engineering-resilience - Fault injection and resilience testing
• visual-testing-advanced - Visual regression and UI testing
• compliance-testing - Regulatory compliance (GDPR, HIPAA, SOC2)
• compatibility-testing - Cross-browser and platform testing
• localization-testing - i18n and l10n testing
• mutation-testing - Test suite effectiveness evaluation
• performance-testing - Load, stress, and scalability testing
• security-testing - OWASP and security vulnerability testing

V3 Domain Skills (14)

• qe-test-generation - AI-powered test synthesis
• qe-test-execution - Parallel execution and retry logic
• qe-coverage-analysis - O(log n) sublinear coverage
• qe-quality-assessment - Quality gates and deployment readiness
• qe-defect-intelligence - ML defect prediction and root cause
• qe-requirements-validation - BDD scenarios and acceptance criteria
• qe-code-intelligence - Knowledge graphs and token reduction
• qe-security-compliance - OWASP and CVE detection
• qe-contract-testing - Pact and schema validation
• qe-visual-accessibility - Visual regression and WCAG
• qe-chaos-resilience - Fault injection and resilience
• qe-learning-optimization - Transfer learning and self-improvement
• qe-iterative-loop - QE iteration patterns
• aqe-v2-v3-migration - Migration guide from v2 to v3

Strategic & Communication (8)

• six-thinking-hats - Edward de Bono's methodology for QE
• brutal-honesty-review - Unvarnished technical criticism
• sherlock-review - Evidence-based investigative code review
• cicd-pipeline-qe-orchestrator - CI/CD quality orchestration
• bug-reporting-excellence - High-quality bug reports
• consultancy-practices - QE consultancy workflows
• quality-metrics - Effective quality measurement
• pair-programming - AI-assisted pair programming

Testing Techniques & Management (9)

• exploratory-testing-advanced - SBTM and RST heuristics
• test-design-techniques - Test design methodologies
• test-data-management - Test data strategies
• test-environment-management - Environment configuration
• test-reporting-analytics - Quality dashboards and KPIs
• testability-scoring - Score code testability
• technical-writing - Documentation practices
• code-review-quality - Context-driven code reviews
• api-testing-patterns - REST and GraphQL testing

n8n Workflow Testing (5) (contributed by @fndlalit)

• n8n-workflow-testing-fundamentals - Execution lifecycle and data flow
• n8n-expression-testing - Expression validation and testing
• n8n-security-testing - Workflow security scanning
• n8n-trigger-testing-strategies - Webhook and event testing
• n8n-integration-testing-patterns - API contract testing for n8n

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🔄 V2 to V3 Migration

V3 provides automatic backward compatibility with V2:

# Check migration status
aqe migrate status

# Run migration with backup
aqe migrate run --backup

# Validate migration
aqe migrate validate

What gets migrated:

• ✅ Memory data (SQLite → AgentDB with HNSW indexing)
• ✅ Configuration files
• ✅ Learned patterns (→ ReasoningBank)
• ✅ Agent mappings (V2 names → V3 equivalents)

V2 Agent	V3 Agent
qe-test-generator	qe-test-architect
qe-coverage-analyzer	qe-coverage-specialist
qe-quality-gate	qe-quality-gate
qe-security-scanner	qe-security-scanner
qe-coordinator	qe-queen-coordinator

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🤖 LLM Provider Configuration

AQE V3 supports multiple LLM providers for maximum flexibility:

Provider	Type	Cost	Best For
Ollama	Local	FREE	Privacy, offline
OpenRouter	Cloud	Varies	300+ models
Groq	Cloud	FREE	High-speed
Claude API	Cloud	Paid	Highest quality
Google AI	Cloud	FREE	Gemini models

# Configure provider
export GROQ_API_KEY="gsk_..."
aqe init --auto

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📖 Documentation

V3 Guides

• V3 Migration Guide - Complete migration instructions
• V3 CLI Reference - All V3 commands
• DDD Architecture - Domain-driven design overview

V2 Documentation (Legacy)

• V2 README - Complete V2 documentation
• Quick Start Guide - V2 quick start
• User Guide - V2 workflows and examples

Feature Guides

• Learning System Guide - ReasoningBank learning
• Pattern Management Guide - Cross-project patterns
• MCP Integration - Claude Code integration

Testing Guides

• Test Generation - AI-powered test creation
• Coverage Analysis - O(log n) gap detection
• Quality Gates - Intelligent validation

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📊 Project Architecture

agentic-qe/
├── v3/                      # V3 DDD Implementation (Main Version)
│   ├── src/
│   │   ├── kernel/          # Shared kernel
│   │   ├── domains/         # 12 bounded contexts
│   │   │   ├── test-generation/
│   │   │   ├── coverage-analysis/
│   │   │   ├── quality-assessment/
│   │   │   └── ...
│   │   ├── routing/         # Agent routing & registry
│   │   ├── mcp/             # MCP server
│   │   └── cli/             # V3 CLI
│   ├── tests/               # 5,600+ tests
│   └── assets/agents/       # 50 QE agent definitions (43 main + 7 subagents)
├── v2/                      # V2 Implementation (Legacy)
│   ├── src/                 # V2 source code
│   ├── tests/               # V2 tests
│   └── docs/                # V2 documentation
├── .claude/
│   ├── agents/v3/           # V3 agent definitions (source)
│   └── skills/              # 15 QE-specific skills
├── docs/                    # Shared documentation
│   ├── plans/               # Migration plans
│   ├── policies/            # Project policies
│   └── v3/                  # V3 specific docs
├── package.json             # Points to v3 (main version)
└── README.md                # This file

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🚀 Development

Setup

# Clone repository
git clone https://github.com/proffesor-for-testing/agentic-qe.git
cd agentic-qe

# Install V3 dependencies
cd v3
npm install

# Build
npm run build

# Run tests
npm test -- --run

V3 Scripts

Script	Description
npm run build	Compile TypeScript
npm test -- --run	Run all tests
npm run cli	Run CLI in dev mode
npm run mcp	Start MCP server

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🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

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📞 Support

• Documentation: docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@agentic-qe.com

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👥 Contributors

Thanks to all the amazing people who have contributed to Agentic QE Fleet!

@proffesor-for-testing Project Lead	@fndlalit QX Partner, Testability	@shaal Core Development	@mondweep Architecture

View all contributors | Become a contributor

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💖 Support the Project

If you find Agentic QE Fleet valuable, consider supporting its development:

	Monthly	Annual (Save $10)
Price	$5/month	$50/year
Benefits	Sponsor recognition, Priority support	All monthly + Featured in README, Roadmap input
Subscribe	Monthly	Annual

View sponsorship details

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🙏 Acknowledgments

V3 is built on the shoulders of giants:

• Claude Flow by @ruvnet - Multi-agent orchestration, MCP integration, swarm coordination
• Agentic Flow by @ruvnet - Agent patterns, learning systems, neural coordination
• Built with TypeScript, Node.js, and better-sqlite3
• HNSW indexing via hnswlib-node
• Inspired by Domain-Driven Design and swarm intelligence
• Integrates with Jest, Cypress, Playwright, k6, SonarQube, and more
• Compatible with Claude Code via Model Context Protocol (MCP)

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Made with ❤️ by the Agentic QE Team

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