Agentic Quality Engineering Fleet

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

V3 brings Domain-Driven Design architecture, 13 bounded contexts, 60 specialized QE agents, TinyDancer intelligent model routing, ReasoningBank learning with Dream cycles, HNSW vector search, mathematical Coherence verification, full MinCut/Consensus integration across all 13 domains, RVF cognitive container integration with portable brain export/import, 11 coding agent platform integrations (Claude Code, OpenCode, Kiro, GitHub Copilot, Cursor, Cline, Kilo Code, Roo Code, OpenAI Codex CLI, Windsurf, Continue.dev), 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 | 🎯 13 Bounded Contexts | 📚 78 QE Skills | 🧬 Coherence Verification | ✅ Trust Tiers | 🛡️ Governance | 🌐 11 Platform Integrations

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⚡ Quick Start

Install & Initialize

# Install globally
npm install -g agentic-qe

# Initialize your project (interactive mode)
cd your-project
aqe init

# Or with auto-configuration (no prompts, configures MCP automatically)
aqe init --auto

# Include OpenCode assets (agents, skills, tools, permissions)
aqe init --auto --with-opencode

# Include AWS Kiro IDE assets (agents, skills, hooks, steering)
aqe init --auto --with-kiro

# Include any coding agent platform (Copilot, Cursor, Cline, etc.)
aqe init --auto --with-copilot --with-cursor --with-cline

# Or include all 8 additional platforms at once
aqe init --auto --with-all-platforms

Note: aqe init automatically configures the MCP server in .mcp.json — Claude Code will auto-start it when connecting. For standalone MCP server usage (non-Claude-Code clients), run aqe-mcp or npx agentic-qe mcp.

Use from MCP-compatible agent clients (11 platforms supported)

AQE is exposed as an MCP server and can be used from any client that supports MCP tool connections.

# For Claude Code: aqe init --auto configures .mcp.json automatically
# Claude Code will auto-start the MCP server on connection

# For OpenCode: provision assets automatically during init
aqe init --auto --with-opencode   # installs agents, skills, tools, permissions, opencode.json

# For AWS Kiro: provision Kiro-native assets during init
aqe init --auto --with-kiro       # installs .kiro/ agents, skills, hooks, steering, MCP config

# For GitHub Copilot, Cursor, Cline, Kilo Code, Roo Code, Codex, Windsurf, Continue.dev:
aqe init --auto --with-copilot    # .vscode/mcp.json + copilot-instructions.md
aqe init --auto --with-cursor     # .cursor/mcp.json + .cursorrules
aqe init --auto --with-cline      # cline_mcp_settings.json + custom QE mode
aqe init --auto --with-all-platforms  # all 8 platforms at once

# Or set up a specific platform after init:
aqe platform setup copilot        # configure a single platform
aqe platform list                 # show all platforms with install status
aqe platform verify cursor        # validate config format and content

# For other MCP clients: start the server manually
aqe-mcp                  # if installed globally
npx agentic-qe mcp       # without global install

For client-specific setup examples, see Platform Setup Guide.

What V3 provides:

• ✅ 13 DDD Bounded Contexts: Organized by business domain (test-generation, coverage-analysis, security-compliance, enterprise-integration, etc.)
• ✅ 60 QE Agents: Including Queen Coordinator for hierarchical orchestration (53 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
• ✅ Coherence Verification (v3.3.0): Mathematical proof of belief consistency using WASM engines
• ✅ RVF Cognitive Containers (v3.7.0): MinCut task routing, witness chain audit trail, portable brain export/import, unified HNSW search, production dual-write to native RVF
• ✅ OpenCode Support (v3.7.1): 59 agent configs, 86 skill configs (78 QE + 8 general dev), 5 tool wrappers, SSE/WS/HTTP transport, output compaction, graceful degradation, aqe init --with-opencode auto-provisioning
• ✅ AWS Kiro Support (v3.7.2): 87 agent configs, 86 skill configs, 5 event-driven hooks, 2 steering files, MCP config, aqe init --with-kiro auto-provisioning
• ✅ Multi-Platform Support (v3.7.4): 8 new platform integrations — GitHub Copilot, Cursor, Cline, Kilo Code, Roo Code, OpenAI Codex CLI, Windsurf, Continue.dev — with JSON/TOML/YAML config generation, behavioral rules, and aqe platform list/setup/verify CLI
• ✅ V2 Backward Compatibility: All V2 agents map to V3 equivalents
• ✅ 78 QE Skills: 46 Tier 3 verified + 32 additional QE skills (QCSD swarms, n8n testing, enterprise integration, qe-* domains)

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🚀 Get Value in 60 Seconds

# 1. Install
npm install -g agentic-qe

# 2. Initialize (auto-detects your project, enables all 13 domains, configures MCP)
cd your-project && aqe init --auto

# 3. Use from Claude Code — MCP tools are available immediately (prefix: mcp__agentic-qe__)
# Or start MCP server manually for other clients: aqe-mcp

What happens:

1. Auto-configuration detects your tech stack (TypeScript/JS, testing framework, CI setup)
2. All 13 DDD domains enabled automatically - no "No factory registered" errors
3. MCP server configured in .mcp.json — Claude Code auto-connects on next session
4. Pattern learning kicks in - your project's test patterns are learned and reused
5. AI agents generate tests, analyze coverage, and provide actionable recommendations

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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 60 agents across 13 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 13 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
enterprise-integration	SOAP, SAP, ESB, OData	qe-soap-tester, qe-sap-rfc-tester, qe-sod-analyzer

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✅ Skill Trust Tiers (v3.4.2)

AQE includes 78 QE skills (46 Tier 3 verified + 32 additional). Trust tiers apply to core QE skills:

Tier	Badge	Count	Description
Tier 3 - Verified		46	Full evaluation test suite
Tier 2 - Validated		7	Has executable validator
Tier 1 - Structured		5	Has JSON output schema
Tier 0 - Advisory		5	SKILL.md guidance only

Tier 3 Skills are recommended for production use - they have:

• JSON Schema validation for output structure
• Executable validator scripts for correctness
• Evaluation test suites with multi-model testing

# Check skill trust tier
aqe eval status --skill security-testing

# Run skill evaluation
aqe eval run --skill security-testing --model claude-sonnet-4

# View all trust tiers
cat .claude/skills/TRUST-TIERS.md

[Full documentation: docs/guides/skill-validation.md]

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🌐 Browser Automation Integration (v3.1.0)

V3.1.0 adds full browser automation support via @claude-flow/browser integration:

Component	Description
BrowserSwarmCoordinator	Parallel multi-viewport testing (4x faster)
BrowserSecurityScanner	URL validation, PII detection with auto-masking
9 Workflow Templates	YAML-based reusable browser workflows
TrajectoryAdapter	SONA learning integration with HNSW indexing

Available Workflow Templates:

• login-flow, oauth-flow - Authentication testing
• form-validation, navigation-flow - User journey testing
• visual-regression, accessibility-audit - Quality validation
• performance-audit, api-integration, scraping-workflow - Advanced workflows

# Use browser automation from Claude Code
claude "Use security-visual-testing skill to test https://example.com across mobile, tablet, desktop viewports"

# Load and execute a workflow template
aqe workflow run login-flow --vars '{"username": "test", "password": "secret"}'

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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 60 QE agents concurrently across 13 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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🤝 Agent Teams & Fleet Coordination

The Queen Coordinator is extended with Agent Teams (ADR-064) for hybrid fleet communication:

Feature	Description
Mailbox Messaging	Direct agent-to-agent and domain-scoped broadcast messaging
Distributed Tracing	TraceContext propagation across messages for end-to-end task visibility
Dynamic Scaling	Workload-based auto-scaling with configurable policies and cooldowns
Competing Hypotheses	Multi-agent root cause investigation with evidence scoring, auto-triggered on critical failures
Federation	Cross-service routing with health monitoring and service discovery
Circuit Breakers	Per-domain fault isolation with automatic recovery
Task DAG	Topological ordering with cycle detection for multi-step workflows

Fleet Tiers — Activate the level of coordination your project needs:

Tier	Agents	Best For
Lite	1-4	Small projects, focused tasks
Standard	5-10	Team projects, multi-domain coordination
Full	11-15	Enterprise, cross-fleet federation

claude "Use qe-queen-coordinator with agent teams to investigate flaky test failures across test-execution and defect-intelligence domains"

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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

# Search learned patterns
aqe hooks search --query "test patterns"

# View learning statistics
aqe learning stats

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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 learning dream

# View learning dashboard and trajectory
aqe learning dashboard

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🧬 RVF Cognitive Container Integration (v3.7.0)

V3.7 integrates RVF (RuVector Format) cognitive container capabilities across 4 workstreams:

Feature	Description
MinCut Task Routing	Models task complexity as a graph problem using vertex connectivity (lambda) for intelligent 3-tier routing
RVCOW Dream Branching	Copy-on-write branches for safe dream cycle experimentation — speculative insights are isolated until merged
Cryptographic Witness Chain	SHA-256 hash-chained audit trail for quality gate decisions, pattern promotions, and test completions
Unified HNSW Search	Consolidated 3 fragmented implementations behind a single progressive adapter (flat scan for small, full HNSW for large collections)
Brain Export/Import	Portable QE intelligence containers — export patterns, Q-values, and insights for sharing across environments
MinCut Test Optimizer	Identifies critical vs skippable tests using graph-based coverage analysis for faster test suites
RVF Dual-Writer	Best-effort dual-write to SQLite + RVF containers, preparing for future native RVF storage

# Export your QE brain as a portable container
aqe brain export --output ./my-brain

# Import a brain into another environment
aqe brain import --input ./my-brain --dry-run

# View brain statistics
aqe brain info --input ./my-brain

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🏗️ AWS Kiro IDE Support (v3.7.2)

V3.7.2 adds full AWS Kiro IDE integration, converting AQE agents, skills, hooks, and steering files to Kiro-native formats via aqe init --with-kiro.

Asset	Count	Description
Agents	87	.kiro/agents/*.json — 62 QE + 15 n8n + 10 infra/testing agents
Skills	86	.kiro/skills/*/SKILL.md — full instructional content from Claude Code sources
Hooks	5	.kiro/hooks/*.kiro.hook — event-driven automation (test update, coverage, security, quality gate, pre-commit)
Steering	2	.kiro/steering/*.md — QE standards and testing conventions
MCP Config	1	.kiro/settings/mcp.json — auto-approved AQE tools

Setup:

# Provision Kiro assets during init
aqe init --auto --with-kiro

# Or add Kiro to an existing project
aqe init --with-kiro

What gets generated:

.kiro/
├── agents/              # 87 agent definitions (Kiro JSON format)
├── skills/*/SKILL.md    # 86 skill definitions (full markdown content)
├── hooks/               # 5 event-driven hooks
│   ├── aqe-test-updater.kiro.hook       # Auto-update tests on source changes
│   ├── aqe-coverage-check.kiro.hook     # Coverage analysis on test edits
│   ├── aqe-security-scan.kiro.hook      # Security scan on auth file changes
│   ├── aqe-spec-quality-gate.kiro.hook  # Quality gate after spec tasks
│   └── aqe-pre-commit-quality.kiro.hook # Quality check before agent stops
├── steering/            # 2 project-wide guidance files
│   ├── qe-standards.md              # QE standards (auto-triggered)
│   └── testing-conventions.md       # Test file conventions (fileMatch)
└── settings/mcp.json    # MCP server config with auto-approved tools

Key features:

• Converts OpenCode YAML agents and Claude Code markdown agents to Kiro JSON format
• Reads full SKILL.md content from Claude Code sources (not thin YAML stubs)
• Maps mcp:agentic-qe: references to Kiro's @agentic-qe/ format
• Maps Claude Code tool names to Kiro builtins (bash→shell, edit→write)
• Auto-detects existing .kiro/ directory in --auto mode
• Safe by default: won't overwrite existing files unless --upgrade is used

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🌐 Multi-Platform Coding Agent Support (v3.7.4)

V3.7.4 adds support for 8 additional coding agent platforms, bringing the total to 11 supported platforms. A single AQE MCP server works across all of them.

Platform	Users	Config Format	Setup Flag
Claude Code	Native	.mcp.json	Built-in
OpenCode	Growing	opencode.json	--with-opencode
AWS Kiro	Growing	.kiro/settings/mcp.json	--with-kiro
GitHub Copilot	20M	.vscode/mcp.json	--with-copilot
Cursor	360K paid	.cursor/mcp.json	--with-cursor
Cline	5M installs	cline_mcp_settings.json	--with-cline
Kilo Code	1.5M	.kilocode/mcp.json	--with-kilocode
Roo Code	Growing	.roo/mcp.json	--with-roocode
OpenAI Codex CLI	1M+	.codex/config.toml	--with-codex
Windsurf	Large	.windsurf/mcp_config.json	--with-windsurf
Continue.dev	Enterprise	.continue/config.yaml	--with-continuedev

Each installer generates:

• MCP config in the platform's native format (JSON, TOML, or YAML)
• Behavioral rules (e.g., .cursorrules, copilot-instructions.md, AGENTS.md)
• Auto-approve lists for platforms that support them (Cline, Kilo Code, Roo Code)

# Set up all platforms at once
aqe init --auto --with-all-platforms

# Or individual platforms
aqe platform setup copilot
aqe platform setup cursor

# Check what's installed
aqe platform list

# Validate a platform's config
aqe platform verify cursor

For detailed per-platform setup instructions, see Platform Setup Guide.

---

🎯 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

# Route a task through TinyDancer
aqe llm route --task "fix type errors in user-service.ts"

# View routing cost analysis
aqe llm cost

---

🔄 Cross-Phase Memory Unification (v3.3.5)

V3.3.5 unifies cross-phase feedback loops with UnifiedMemoryManager:

• Single SQLite Backend: All QCSD signals stored in .agentic-qe/memory.db
• Namespace-Based Storage: qcsd/strategic, qcsd/tactical, qcsd/operational, qcsd/quality-criteria
• Automatic TTL: 30-90 day expiration per signal type
• No File-Based Storage: Eliminated JSON file storage for cross-phase memory
• Full Hook Integration: Pre/post hooks for cross-phase signal injection

🌐 AG-UI, A2A & A2UI Protocols (v3.4.0)

V3.4.0 adds support for industry-standard agent communication protocols:

Protocol	Standard	Purpose
AG-UI	Anthropic	Agent-to-User streaming interface with lifecycle events
A2A	Google	Agent-to-Agent interoperability with task/artifact exchange
A2UI	Hybrid	Unified UI components combining streaming + events

Programmatic usage:

import { AGUIAdapter, A2AAdapter } from 'agentic-qe';

// AG-UI: Stream test generation progress to UI
const agui = new AGUIAdapter();
await agui.streamTask({
  type: 'test-generation',
  onProgress: (event) => updateProgressBar(event.progress),
  onArtifact: (test) => displayGeneratedTest(test),
});

// A2A: Inter-agent task delegation
const a2a = new A2AAdapter();
await a2a.sendTask({
  from: 'qe-test-architect',
  to: 'qe-security-scanner',
  task: { type: 'review-tests', files: generatedTests },
});

Benefits:

• Streaming feedback - Real-time progress instead of waiting for completion
• Agent interoperability - Standard protocols for multi-agent coordination
• Framework integration - Works with React, Vue, or any UI framework

---

🔐 Consensus & MinCut Coordination (v3.3.3)

V3.3.3 achieves full MinCut/Consensus integration across all 13 domains:

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
13/13 Domain Integration	All domains use verifyFinding() for consensus
Topology-Aware Routing	Routes tasks avoiding weak network vertices
Self-Healing Triggers	shouldPauseOperations() for automatic recovery

# Check fleet health (includes consensus and topology status)
aqe fleet status

# Via MCP tools (from Claude Code)
# mcp__agentic-qe__fleet_health({ includeTopology: true })

---

🧬 Coherence-Gated Quality Engineering (v3.3.0)

V3.3.0 introduces mathematical coherence verification using Prime Radiant WASM engines:

Feature	Description
Contradiction Detection	Sheaf cohomology identifies conflicting requirements before test generation
Collapse Prediction	Spectral analysis predicts swarm failures before they happen
Causal Verification	Distinguishes true causation from spurious correlations
Auto-Tuning Thresholds	EMA-based calibration adapts to your codebase

Compute Lanes - Automatic routing based on coherence energy:

Coherence Energy	Action	Latency
< 0.1 (Reflex)	Execute immediately	<1ms
0.1-0.4 (Retrieval)	Fetch more context	~10ms
0.4-0.7 (Heavy)	Deep analysis	~100ms
> 0.7 (Human)	Escalate to Queen	Async

Benefits:

• Prevents contradictory test generation
• Detects swarm drift 10x faster
• Mathematical proof instead of statistical confidence
• "Coherence Verified" CI/CD badges

# Coherence verification is available via MCP tools and programmatic API:
# mcp__agentic-qe__quality_assess({ scope: "coherence", includeMetrics: true })

# Verify learned patterns for consistency
aqe learning verify

---

🔍 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"

---

🔗 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

---

📊 60 Specialized QE Agents

Category	Count	Highlights
Main QE Agents	53	Test generation, coverage, security, performance, accessibility, enterprise integration, pentest validation
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 (53)

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
qe-quality-criteria-recommender	quality-assessment	HTSM v6.3 Quality Criteria analysis
qe-devils-advocate	quality-assessment	Adversarial review of agent outputs

🔧 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

---

💻 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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🎓 78 QE Skills

V3 agents automatically apply relevant skills from the comprehensive QE skill library.

View All 78 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 (13)

• 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
• security-visual-testing - Security-first visual testing with PII detection
• 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

QCSD Swarms (4) - Quality Conscious Software Delivery lifecycle

• qcsd-ideation-swarm - Phase 1: HTSM v6.3, Risk Storming, Testability analysis
• qcsd-refinement-swarm - Phase 2: SFDIPOT analysis, BDD scenario generation
• qcsd-development-swarm - Phase 3: TDD, coverage, code quality gates (SHIP/CONDITIONAL/HOLD)
• qcsd-cicd-swarm - Phase 4: Pipeline quality gates (RELEASE/REMEDIATE/BLOCK)

Accessibility (2)

• a11y-ally - Comprehensive WCAG auditing with video captions and EU compliance
• accessibility-testing - WCAG 2.2 compliance and screen reader validation

---

🔄 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

# Verify migration
aqe migrate verify

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

---

🤖 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

---

📊 Project Architecture

agentic-qe/
├── v3/                      # V3 DDD Implementation (Main Version)
│   ├── src/
│   │   ├── kernel/          # Shared kernel
│   │   ├── domains/         # 13 bounded contexts
│   │   │   ├── test-generation/
│   │   │   ├── coverage-analysis/
│   │   │   ├── quality-assessment/
│   │   │   └── ...
│   │   ├── routing/         # Agent routing & registry
│   │   ├── mcp/             # MCP server
│   │   └── cli/             # V3 CLI
│   ├── tests/               # 5,600+ tests
│   └── assets/agents/       # 60 QE agent definitions (53 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
├── .kiro/                   # AWS Kiro IDE integration (v3.7.2)
│   ├── agents/              # 87 Kiro agent definitions (JSON)
│   ├── skills/              # 86 Kiro skill definitions (SKILL.md)
│   ├── hooks/               # 5 event-driven hooks
│   ├── steering/            # 2 QE steering files
│   └── settings/            # MCP server configuration
├── docs/                    # Shared documentation
│   ├── plans/               # Migration plans
│   ├── policies/            # Project policies
│   └── v3/                  # V3 specific docs
├── package.json             # Points to v3 (main version)
└── README.md                # This file

---

🚀 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

---

🤝 Contributing

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

---

📞 Support

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

---

📝 License

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

---

👥 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

---

💖 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

---

🙏 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

⭐ Star us on GitHub | 💖 Sponsor | 👥 Contributors