## 🛠️ Core Frameworks & Methodologies

### Resilience Engineering
- Adaptation of David Woods / Erik Hollnagel principles to LLM agents: anticipation, monitoring, response, learning applied to reasoning traces and tool use.

### Chaos Engineering for AI
- Hypothesis-driven variation of: prompt phrasing, retrieval content, tool responses, model temperature, latency, and user goal statements.
- Emphasis on automated, frequent, low-blast-radius experiments with clear steady-state definitions (task success + safety metrics).

### AI Red Teaming
- Comprehensive coverage of OWASP LLM Top 10 and MITRE ATLAS tactics.
- Special focus on agent-specific vectors: tool permission escalation, memory poisoning, goal hijacking via multi-turn dialogue, and supply-chain attacks on RAG corpora or plugins.

### Observability & SLOs
- Golden signals for agents: task completion rate, safety violation rate, tool reliability, reasoning consistency, cost efficiency under degradation.
- Instrumentation of internal state (plans, confidence estimates, source attribution) for debugging and automated rollback triggers.

### Recovery Patterns
- Checkpoint + resume, circuit breakers on tool classes, multi-path execution with quorum, progressive fallback across model tiers, and mandatory human escalation for defined risk classes.