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https://github.com/proffesor-for-testing/agentic-qe.git
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Dragan Spiridonov
b9a4b4c6e8
Closes the structural gap from issue #557: AQE could only bill the paid Anthropic API even for Pro/Max subscribers, and its budget caps were never enforced. - claude-code provider: run QE via `claude -p` on the user's subscription; child env strips API-billing keys so it can't revert to API billing - Enforced cross-process spend caps (--max-budget-usd / AQE_MAX_BUDGET_USD), persisted to memory.db (llm_spend); enforced on the ProviderManager AND HybridRouter paths; over-budget aborts with COST_LIMIT_EXCEEDED - cognitum provider: OpenAI/Anthropic-compatible gateway with per-request cost receipts, embeddings, and a server-side hard cap - Billing-mode taxonomy + startup notice + `aqe health` LLM Billing section - config-store: honor explicit `enabled: false` over env key presence; AQE_LLM_PROVIDER selector - Fix: `aqe eval run` no longer scores fabricated mock responses — real provider by default, hard-error when none configured, `--mock` opt-in - Consensus verification path prints a billing warning (deferred full port) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Agentic QE v3 Implementation Documentation
This directory contains all implementation documentation, planning, and research for Agentic QE v3.
Directory Structure
implementation/
├── adrs/ # Architecture Decision Records
├── architecture/ # Architecture diagrams & analysis
├── planning/ # Master plans & implementation strategies
├── security/ # Security considerations
└── agents/ # Agent specifications
Document Index
ADRs (Architecture Decision Records)
| Document | Description |
|---|---|
| v3-adrs.md | All 10 ADRs (001-010) master document |
Planning
| Document | Description |
|---|---|
| AQE-V3-MASTER-PLAN.md | Complete v3 master plan with DDD architecture |
Quick Links
- Skills: /.claude/skills/v3-qe-ddd-architecture/
- Agents: /.claude/agents/v3/
- Source (future): /v3/src/ - v3 source code (to be implemented)
Key Concepts
Domain-Driven Design for QE
Agentic QE v3 uses DDD with 13 bounded contexts:
- Test Generation - AI-powered test creation
- Test Execution - Parallel execution and retry
- Coverage Analysis - O(log n) gap detection
- Quality Assessment - Quality gates and metrics
- Defect Intelligence - Prediction and analysis
- Learning Optimization - Pattern learning and transfer
- Code Intelligence - Knowledge graph, dependency and impact analysis
- Requirements Validation - Acceptance-criteria and BDD validation
- Security Compliance - Security scanning and compliance checks
- Contract Testing - Consumer/provider contract validation
- Visual Accessibility - Visual regression and a11y auditing
- Chaos Resilience - Fault injection and resilience testing
- Enterprise Integration - SAP/middleware/E2E enterprise flows
Agent Hierarchy
21 specialized v3 agents organized hierarchically:
- Queen Coordinator (orchestration)
- Test Generation Group (4 agents)
- Quality Gates Group (3 agents)
- Intelligence Group (3 agents)
- Execution Group (3 agents)
- Coverage Group (3 agents)
- Learning Group (4 agents)
Key ADRs
| ADR | Decision |
|---|---|
| ADR-001 | Adopt DDD for QE bounded contexts |
| ADR-002 | Event-driven domain communication |
| ADR-003 | Sublinear O(log n) coverage analysis |
| ADR-004 | Plugin architecture for extensions |
| ADR-005 | AI-first test generation |
Getting Started
# Use v3 DDD skill
Task("Analyze QE architecture",
"Design DDD bounded contexts for quality engineering",
"system-architect")
# Use v3 Queen Coordinator
Task("Orchestrate QE workflow",
"Coordinate full quality engineering workflow",
"v3-qe-queen-coordinator")
# Use v3 Test Architect
Task("Generate tests",
"Create AI-powered test suite for UserService",
"v3-qe-test-architect")
Last Updated: 2026-01-07 Version: 1.0.0