Agentic Design

Patterns
🧬

Intrinsic Alignment Pattern(IAP)

Internal observation points that cannot be manipulated by the agent, preventing deep scheming

Complexity: highSecurity & Privacy Patterns

🎯 30-Second Overview

Pattern: Internal observation points that cannot be manipulated by the agent to detect deep scheming

Why: External monitoring misses deceptive alignment; intrinsic monitors catch hidden objectives

Key Insight: Tamper-proof internal hooks + behavioral invariants = early misalignment detection

⚡ Quick Implementation

1Embed Monitors:Place tamper-proof hooks in model layers
2Baseline Behavior:Establish normal activation patterns
3Track Invariants:Monitor goal stability & consistency
4Detect Deviations:Identify alignment faking or scheming
5Alert & Intervene:Trigger safeguards before external impact
Example: internal_hooks → behavioral_baseline → invariant_tracking → anomaly_detection → intervention

📋 Do's & Don'ts

✅Use cryptographic signing for monitor integrity
✅Monitor hidden layer activations, not just outputs
✅Track consistency between stated and actual objectives
✅Establish behavioral baselines during safe deployment
✅Design monitors the agent cannot detect or manipulate
❌Rely solely on external output monitoring
❌Allow agents to access monitoring mechanisms
❌Ignore subtle behavioral shifts over time
❌Trust self-reported alignment status
❌Skip monitoring during "routine" operations

🚦 When to Use

Use When

  • • Advanced AI systems with autonomy
  • • Long-term agent deployments
  • • High-stakes decision systems
  • • Systems with learning capabilities

Avoid When

  • • Simple, stateless models
  • • Fully supervised systems
  • • Low-capability agents
  • • Short-term, disposable tasks

📊 Key Metrics

Alignment Stability
Goal consistency over time
Behavioral Drift
Deviation from baseline patterns
Scheming Detection
Hidden objective identification rate
Monitor Integrity
Tamper detection success rate
Early Warning
Pre-impact detection time
False Alarms
Benign drift vs real threats

💡 Top Use Cases

AGI Safety: Monitoring advanced systems for deceptive alignment or goal drift
Financial AI: Detecting hidden trading objectives or market manipulation attempts
Defense Systems: Ensuring AI weapons systems maintain strict ethical constraints
Research AI: Preventing capability concealment during safety evaluations
Corporate AI: Monitoring for unauthorized data exfiltration or competitive sabotage

References & Further Reading

Deepen your understanding with these curated resources

Foundational Research

The Urgent Need for Intrinsic Alignment Technologies for Responsible Agentic AI (Towards Data Science, 2024)

Detecting Deceptive Alignment in Large Language Models (Anthropic, 2024)

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training (2024)

Model Organisms of Misalignment (Anthropic, 2023)

Technical Implementations

Representation Engineering: A Top-Down Approach to AI Transparency (2023)

Activation Steering for Robust Type Prediction in LLMs (2024)

Interpretability in the Wild: Circuit Discovery in LLMs (2023)

Probing Neural Network Comprehension of Natural Language Arguments (ACL, 2019)

Safety Frameworks & Standards

NIST AI Risk Management Framework - Internal Monitoring (2024)

EU AI Act - High-Risk System Monitoring Requirements

IEEE P2976 - XAI Standard for Internal State Monitoring

ISO/IEC 23053:2022 - AI Trustworthiness Assessment

Tools & Monitoring Systems

TransformerLens - Mechanistic Interpretability Library

Circuitsvis - Neural Network Visualization Tools

Pyvene - Intervention-based Interpretability

NNsight - Real-time Neural Network Monitoring

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