Agentic Design

Patterns
🛡️

GuardAgent Pattern(GAP)

Dedicated guardrail agent monitoring and protecting target agents through dynamic safety checks

Complexity: highSecurity & Privacy Patterns

🎯 30-Second Overview

Pattern: Dedicated guard agent monitors target agents through dynamic safety check generation

Why: Self-monitoring fails; external validation with deterministic code ensures 98%+ accuracy

Key Insight: Safety requirements → Task plan → Executable code → Real-time enforcement

⚡ Quick Implementation

1Analyze Request:Parse safety guard requirements & constraints
2Generate Plan:Create task plan from safety requirements
3Map to Code:Convert plan into executable guardrail code
4Execute & Monitor:Run code to validate agent actions
5Block/Allow:Deterministic decision based on validation
Example: safety_analysis → task_planning → code_generation → execution → enforcement

📋 Do's & Don'ts

✅Use dedicated monitoring agents separate from target agents
✅Generate deterministic code for consistent enforcement
✅Implement comprehensive action logging and audit trails
✅Define clear safety requirements in natural language
✅Test guardrails independently before deployment
❌Let target agents self-monitor without external validation
❌Rely on probabilistic checks for critical safety
❌Skip validation of generated guardrail code
❌Allow guardrail bypass for "trusted" operations
❌Ignore performance impact on target agent latency

🚦 When to Use

Use When

  • • Autonomous agent deployments
  • • High-risk operations
  • • Compliance-critical systems
  • • Multi-agent coordination

Avoid When

  • • Simple, low-risk tasks
  • • Extreme latency requirements
  • • Stateless operations only
  • • Resource-constrained environments

📊 Key Metrics

Guardrail Accuracy
% correct safety decisions (98%+)
False Positives
Valid actions blocked incorrectly
Response Time
ms to validate each action
Code Generation
Time to create guardrail logic
Coverage
% of agent actions monitored
Violation Rate
Safety violations per 1000 actions

💡 Top Use Cases

Financial Trading: Monitor trades for limits, restricted securities, risk thresholds
Healthcare AI: Validate medical recommendations against safety protocols & regulations
Autonomous Systems: Ensure robots/vehicles operate within physical & ethical bounds
Content Generation: Block harmful, biased, or policy-violating outputs in real-time
Data Processing: Prevent unauthorized access, ensure privacy compliance in pipelines

References & Further Reading

Deepen your understanding with these curated resources

Primary Research

GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning (ArXiv:2406.09187, 2024)

Constitutional AI: Harmlessness from AI Feedback (Anthropic, 2022)

Red Teaming Language Models with Language Models (Anthropic, 2022)

ReAct: Synergizing Reasoning and Acting in Language Models (2023)

Safety Benchmarks & Evaluations

SafetyBench: Evaluating Safety of Large Language Models

TrustLLM: Trustworthiness in Large Language Models (2024)

HELM: Holistic Evaluation of Language Models - Safety Metrics

AI Safety Benchmark from MLCommons

Implementation Frameworks

LangChain - Agent Supervision and Monitoring

AutoGen - Multi-Agent Conversation with Safety

LlamaIndex - Agent Monitoring and Control

Guidance - Constraint-based Generation

Industry Best Practices

OpenAI Safety Best Practices - Multi-layer Defense

Google DeepMind - Sparrow Agent Safety

Meta AI - Responsible AI Practices for Agents

Microsoft Responsible AI - Agent Monitoring

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