Apply context-driven testing principles where practices are chosen based on project context, not universal 'best practices'. Use when making testing decisions, questioning dogma, or adapting approaches to specific project needs.
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<default_to_action> When making testing decisions or adapting approaches:
Quick Context Analysis:
Critical Success Factors:
| Heuristic | Application |
|---|---|
| SFDIPOT | Structure, Function, Data, Interfaces, Platform, Operations, Time |
| Oracles | Consistency with history, similar products, expectations, docs |
| Tours | Business District, Historical, Bad Neighborhood, Tourist, Museum |
Startup Context:
Enterprise Context:
Regulated (FDA/medical):
Fast-paced startup:
// Agent analyzes context and recommends approach
const context = await Task("Analyze Context", {
project: 'e-commerce-platform',
stage: 'startup',
constraints: ['timeline: tight', 'budget: limited'],
risks: ['payment-security', 'high-volume']
}, "qe-fleet-commander");
// Context-aware agent selection
// - qe-security-scanner (critical risk)
// - qe-performance-tester (high volume)
// - Skip: qe-visual-tester (low priority in startup context)
// Adaptive testing strategy
await Task("Generate Tests", {
context: 'startup',
focus: 'critical-paths-only',
depth: 'smoke-tests',
automation: 'minimal'
}, "qe-test-generator");
aqe/context-driven/
├── context-analysis/* - Project context snapshots
├── decisions/* - Testing decisions with rationale
├── discoveries/* - What was learned during testing
└── adaptations/* - How approach changed over time
const contextFleet = await FleetManager.coordinate({
strategy: 'context-driven',
context: {
type: 'greenfield-saas',
stage: 'growth',
compliance: 'gdpr-only'
},
agents: ['qe-test-generator', 'qe-security-scanner', 'qe-performance-tester'],
exclude: ['qe-visual-tester', 'qe-requirements-validator'] // Not priority
});
With Agents: Agents analyze context, adapt strategies, and learn what works in your situation. Use agents to scale context-driven thinking while maintaining human judgment for critical decisions.
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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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