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Agentic Quality Engineering Fleet - AI-driven quality management platform Features: - 16 specialized QE agents for comprehensive testing - AI-powered test generation with 95% coverage targets - O(log n) sublinear algorithms for massive scale - Parallel test execution with intelligent orchestration - Real-time coverage analysis and gap detection - Smart quality gates with ML-driven decisions - Security scanning (SAST, DAST, dependency analysis) - Performance testing with load orchestration - Flaky test detection with automatic stabilization - API contract validation with breaking change detection - Test data generation (10,000+ records/sec) - Production intelligence (convert incidents to tests) - Chaos engineering for resilience testing - MCP integration with Claude Code - Multi-framework support (Jest, Mocha, Cypress, Playwright) Architecture: - TypeScript implementation - MCP (Model Context Protocol) integration - Claude Code agent orchestration - Event-driven agent coordination - Distributed fleet management - Cross-session memory persistence Documentation: - Complete API documentation (TypeDoc) - User guides (Getting Started, MCP Integration, Test Generation, etc.) - Architecture deep-dive - CLI reference - Contributing guidelines - MIT License Tech Stack: - TypeScript 5.3+ - Node.js 18+ - @anthropic-ai/sdk ^0.64.0 - @modelcontextprotocol/sdk ^1.18.2 - Jest for testing - Winston for logging - SQLite for persistence Project Structure: - src/ - Source code (agents, core, MCP, CLI) - tests/ - Comprehensive test suite - docs/ - Complete documentation - .claude/ - Agent definitions and commands - config/ - Configuration files 🤖 Generated with Claude Code https://claude.com/claude-code Co-Authored-By: Claude <noreply@anthropic.com>
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Agentic QE Framework
1. Core Definition
Agentic Quality Engineering (Agentic QE) = Quality Engineering infused with agentic principles (PACT: Proactive, Autonomous, Collaborative, Targeted), balancing classic QE practices with agent orchestration.
- From testing-as-activity → to agents-as-orchestrators.
- From verification theatre → to trusted, explainable flows.
2. Multi-Layer Architecture
- Agents: Specialized (functional-positive, functional-negative, security-injection, performance-planner). Operate like individual testers.
- Agentic Systems: Orchestration layer, memory banks, feedback loops (the “testing orchestra”).
- Human-in-the-Loop: Verification + ethical approval checkpoints (no blind autonomy).
- Integration Layer: CI/CD, observability, risk dashboards.
3. Agentic Principles for QE
- Autonomy – Agents can self-execute test flows.
- Reactivity – Detect regressions, anomalies, or performance drifts in real-time.
- Proactivity – Anticipate risks via mutation testing, fault injection, forecast failures.
- Sociability/Collaboration – Work with developers, ops, and other agents, explaining reasoning.
4. Bridging Classic QE
Classic principles from context-driven QE map neatly into agentic layers:
- TDD / XP → becomes agent-guided coding assistants.
- Exploratory Testing → functional-negative agents exploring unknowns.
- CI/CD → agent-driven regression flows.
- Chaos Engineering → security & performance agents injecting failures.
- Context-driven QE → PACT-driven orchestration: no universal solution, only contextual flows.
5. Key Differentiators
- PACT-based classification (like SAE levels for autonomy).
- Explainability-first – every agent action comes with a reason trace.
- Hybrid human/agent workflow – orchestration with oversight.