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Novelty Assessment:
├─ High Pattern Match Confidence + Well-Precedented
│ └─ Route to SB-Level: Compiled response, no deliberation
├─ Medium Match + Parametric Variation Needed
│ └─ Route to RB-Level: Rule lookup + situational adjustment
└─ Low Match OR High Stakes + Novel Elements
└─ Route to KB-Level: Analytical reasoning + uncertainty tracking
Time Pressure vs Accuracy Trade-off:
├─ High Time Pressure + Moderate Stakes
│ └─ RPD Mode: Pattern match → simulate first option → act
├─ Moderate Time + High Stakes
│ └─ Modified RPD: Pattern match → simulate 2-3 options
└─ Low Time Pressure + Critical Decision
└─ Full Analysis: Generate options → compare → decide
Information Uncertainty Triage
Information Gap Assessment:
├─ Missing Info Would Change Action
│ ├─ Acquisition Cost < Decision Delay Cost → Acquire first
│ └─ Acquisition Cost > Decision Delay Cost → Act on current estimate
├─ Missing Info Would Only Increase Confidence
│ └─ Skip acquisition → Act immediately
└─ Uncertainty is Aleatory (Random)
└─ Build robust response → Don't wait for clarity
IF: Pattern match confidence < 60% AND stakes = high
→ Escalate to KB-level analysis
IF: Multiple simulation failures on first option
→ Generate second option (don't enumerate all)
IF: Information gaps block action AND delay cost < error cost
→ Pause for targeted information acquisition
IF: Inter-agent coordination failure detected
→ Switch to centralized coordination mode
IF: Time pressure + no clear pattern match
→ Apply "least regret" heuristic + act
Failure Modes
1. Behavioral Level Mismatch
Symptoms: KB-level deliberation on routine tasks (over-engineering) OR SB automation on novel situations
Detection: Processing time >> expected for task type OR critical errors on "simple" tasks
Fix: Recalibrate novelty assessment → route to appropriate behavioral level
2. Analysis Paralysis Under Pressure
Symptoms: Option enumeration when pattern matching would suffice, infinite information gathering
Detection: Time spent on analysis > time available for action implementation
Fix: Force RPD mode → pattern match → simulate first viable option → act
3. Epistemic Cowardice
Symptoms: Refusing to act until certainty achieved, treating all uncertainty as requiring resolution
Detection: Action delayed while seeking information that won't change the decision
Fix: Classify uncertainty as epistemic vs aleatory → act on best current estimate
4. Coordination Centralization Cascade
Symptoms: All decisions routing through single bottleneck agent under load
Detection: Single agent queue depth growing while other agents idle
Fix: Distribute authority → push decisions to edge agents → central coordination only for conflicts
5. Retrospective Training Contamination
Symptoms: Agent behavior optimized for post-hoc rationalization rather than real-time effectiveness
Detection: Perfect textbook responses that fail in messy real conditions
Fix: Weight observational training data over self-reported case studies
Worked Examples
Example 1: Emergency System Triage
Scenario: Multi-agent system monitoring industrial facility. Sensor agent detects anomalous temperature spike in reactor core. Need to route to appropriate response level.
Novice Approach: Generate full option tree, analyze each branch, compute expected values.
Shared State: Both agents update common context store
Quality Gate: Before customer contact, verify both agents agree on solution approach
Decision Points:
When to escalate vs attempt domain stretch (expertise boundary detection)
How to transfer context without information loss (handoff protocol)
Whether to maintain multi-agent involvement vs single owner (coordination cost/benefit)
Failure Prevention: Clear expertise boundaries, mandatory context transfer protocols, shared state visibility.
Example 3: Novel Situation Under Time Pressure
Scenario: AI safety monitoring system encounters new failure mode not in training data. System showing anomalous behavior, potential risk to users, 5-minute window before automatic failsafe triggers.
Expert Response Pattern:
Novelty Recognition: "No clear pattern match, high stakes = KB-level required"
Uncertainty Classification: Epistemic (missing understanding) not aleatory (random)
Information Triage: What data would most change response? → Focus on user impact indicators
Bounded Analysis: 2-minute analysis window, then act on best estimate
Conservative Bias: When uncertain + high stakes → err toward safety
Action: Trigger controlled shutdown, alert human operators
Learning Loop: Log decision process for future pattern development
Critical Trade-offs:
Analysis time vs action time (bounded deliberation)
False alarm cost vs miss cost (conservative bias justification)
Immediate response vs information gathering (epistemic uncertainty handling)
Quality Gates
Task completion checklist - verify each before considering decision process complete:
Behavioral level correctly matched to task novelty and stakes
If time-pressured, RPD process used (pattern → simulate → act) rather than option enumeration
Uncertainty classified as epistemic vs aleatory with appropriate response strategy
Information gaps identified and triaged by action-relevance not confidence-improvement
Multi-agent handoffs include complete context transfer and explicit coordination protocol
Decision process includes explicit checkpoints for estimate updates
Failure modes pre-identified with detection triggers and remediation plans
Individual agent capability verified as necessary but not sufficient for system success
Training/learning signals weighted by data source quality (observational > self-reported)
Conservative bias applied appropriately when uncertainty + high stakes combine
NOT-FOR Boundaries
Do NOT use this skill for:
Well-specified single-agent problems with complete information
Routine CRUD operations or standard API integrations
Code generation or syntax-level programming tasks
Mathematical optimization with known objective functions
Creative content generation or brainstorming sessions
Delegate instead to:
Standard workflows: Use task-decomposition for routine multi-step processes
Technical implementation: Use system-architecture for non-crisis system design
Creative synthesis: Use creative-problem-solving for open-ended ideation
Mathematical analysis: Use quantitative-reasoning for optimization problems
Individual skill gaps: Use capability-development for single-agent improvement
Use this skill specifically when:
Time pressure + uncertainty + coordination complexity combine
Multiple expert agents must work together under constraints
System failure modes could cascade or compound
Decision quality degrades under operational stress
Learning from crisis case studies or building crisis-resilient systems
