Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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Apply the Holistic Testing Model evolved with PACTS (Proactive, Autonomous, Collaborative, Targeted, Structured) principles. Use when designing comprehensive test strategies for Classical, AI-assisted, Agent based, or Agentic Systems building quality into the team, or implementing whole-team quality practices.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
<default_to_action> When designing test strategies or building quality into teams:
Quick PACTS Application:
Critical Success Factors:
| Principle | Focus | Anti-Pattern |
|---|---|---|
| Proactive | Test before code, design testability | Waiting for bugs to find you |
| Autonomous | Teams deploy when ready | QA as manual gatekeepers |
| Collaborative | Whole-team quality thinking | QA works in isolation |
| Targeted | Risk-based, high-value tests | Exhaustive checkbox testing |
| Structured | Governance, observability, explainability of agent behavior | Bolting agentic AI onto chaos |
| Quadrant | Purpose | Examples |
|---|---|---|
| Tech + Support | Fast feedback | Unit, component, integration tests |
| Tech + Critique | Find limits | Performance, security, chaos |
| Business + Support | Shared understanding | BDD, acceptance tests |
| Business + Critique | Discover unknowns | Exploratory, usability, A/B |
| PACTS Dimension | Agents |
|---|---|
| Proactive + Tech | qe-test-generator, qe-requirements-validator |
| Autonomous + Tech | qe-test-executor, qe-coverage-analyzer |
| Collaborative | qe-fleet-commander (orchestration) |
| Targeted | qe-regression-risk-analyzer, qe-quality-gate |
| Structured | qe-fleet-commander, qe-quality-gate (governance, observability, explainability) |
// During API design, ask: "How will we know if this times out under load?"
// Build observability from start
await Task("Risk Analysis", {
phase: 'refinement',
question: 'What could go wrong and how will we know?'
}, "qe-requirements-validator");
// E-commerce checkout? Test thoroughly.
// Admin panel used twice a month? Lighter touch.
await Task("Risk-Based Planning", {
critical: ['checkout', 'payment'],
light: ['admin-panel', 'settings']
}, "qe-regression-risk-analyzer");
The 5th principle for agentic systems. As agents take on quality work, you need governance (boundaries and guardrails), observability (what agents did and why), and explainability (decisions you can audit). Especially important in regulated industries.
// Make agent behavior auditable and explainable, not a black box
await Task("Agent Governance Review", {
guardrails: ['scope-boundaries', 'approval-gates'],
observability: ['decision-traces', 'confidence-scores'],
explainability: 'every-decision-auditable'
}, "qe-fleet-commander");
PACT originated with Reuven Cohen (Agentics Foundation) and was adapted to quality engineering by Dragan Spiridonov. PACTS adds a fifth principle — Structured — inspired by DORA's research on AI-assisted delivery and platform-engineering quality measurement. The addition reflects that autonomous, agentic quality work needs governance, observability, and explainability to be trustworthy at scale.
| Old Way (Sequential) | Holistic + PACTS (Concurrent) |
|---|---|
| Dev writes → QA tests → bugs found → fixes | Team discusses what to build and how to test |
| Slow feedback, finger-pointing | Fast feedback, shared ownership |
| Quality as gatekeeping | Quality as enabler |
| QA on critical path | QA builds infrastructure, coaches |
aqe/holistic-testing/
├── pacts-assessment/* - PACTS maturity analysis
├── quadrant-coverage/* - Coverage per quadrant
├── team-metrics/* - Quality ownership metrics
└── feedback-loops/* - Cycle time data
const holisticFleet = await FleetManager.coordinate({
strategy: 'holistic-testing',
pacts: { proactive: true, autonomous: true, collaborative: true, targeted: true, structured: true },
agents: [
'qe-fleet-commander', // Orchestration
'qe-test-generator', // Tech quadrant
'qe-requirements-validator', // Business quadrant
'qe-quality-analyzer' // Metrics
],
topology: 'mesh'
});
PACTS = Proactive + Autonomous + Collaborative + Targeted + Structured
Quality is built in, not tested in. Teams own quality. QA enables, doesn't gate. Test what matters, skip what doesn't. Govern agent behavior with observability and explainability — measure confidence, not trust. Measure outcomes, not activities.
With Agents: Agents analyze PACTS maturity, recommend quadrant coverage, and coordinate whole-team quality. Use agents to scale holistic thinking while maintaining human judgment.
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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