For Engineers

For Engineers

You want to build safe AI systems. Here’s the fastest path to implementation.

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Skip the Theory Path (15 min)

If you trust the patterns and just want to implement:

1. Quick Reference — Core formulas and decision flowchart (5 min)
2. Quick Start Guide — Step-by-step implementation checklist (10 min)
3. Minimal Framework — The 5 essential patterns (5 min)

You can stop here and have a working safety layer.

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Understand-Then-Build Path (2 hours)

If you want to understand why the patterns work:

1. Five-Minute Intro — Core problem (5 min)
2. Core Concepts — Visual framework (20 min)
3. Delegation Risk Overview — Quantitative foundation (30 min)
4. Design Patterns Index — Pattern catalog (15 min)
5. Composing Patterns — Pattern combinations (20 min)
6. Pick your patterns — Based on your threat model (20 min)

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Pattern Categories for Engineers

Structural Patterns (How you architect)

Pattern	What It Does	When to Use
Least-X Principles	Minimize permissions, context, autonomy	Always (default)
Structural Patterns	Bulkheads, firewalls, blast radius containment	Multi-component systems
Decomposed Coordination	Separation of powers, rotating validators	High-stakes decisions

Verification Patterns (How you check)

Pattern	What It Does	When to Use
Verification Patterns	Ghost checker, consistency triangulation	Output validation
Multi-Agent Verification	Cross-validation, adversarial collaboration	AI-on-AI verification
Monitoring Patterns	Tripwires, anomaly detection	Runtime safety

Recovery Patterns (When things go wrong)

Pattern	What It Does	When to Use
Recovery Patterns	Checkpoint-rollback, graceful degradation	Any production system
Safety Mechanisms	Kill switches, capability sunset	High-autonomy systems

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Implementation Checklist

Phase 1: Risk Assessment (Day 1)

[ ] List all AI components in your system
[ ] For each component, identify:
    - What data can it access?
    - What actions can it take?
    - What's the worst-case outcome?
[ ] Calculate rough risk budget (see Quick Reference)
[ ] Classify as Low / Medium / High risk

Phase 2: Essential Safety (Week 1)

[ ] Implement Least Privilege
    - Restrict file access to workspace only
    - Remove unnecessary API permissions
    - Use read-only where possible

[ ] Implement Human Escalation
    - Define escalation thresholds ($, sensitivity, confidence)
    - Build approval workflow
    - Test the escalation path

[ ] Implement Output Filtering
    - PII detection
    - Policy violation checks
    - Harmful content filtering

[ ] Implement Sandboxing
    - Containerized execution
    - Network isolation
    - Resource limits

[ ] Implement Audit Logging
    - All inputs and outputs
    - Decision traces
    - Timestamps and versions

Phase 3: Enhanced Safety (Month 1)

[ ] Add verification layer (if high-risk)
[ ] Set up monitoring and alerting
[ ] Create incident response runbook
[ ] Schedule quarterly risk reviews

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Code-Adjacent Examples

The framework includes worked examples you can reference:

Example	Type	Key Patterns
Research Assistant	Multi-step task	Escalation, checkpoints
Trading System	High-stakes, autonomous	Risk budgets, circuit breakers
Healthcare Bot	Safety-critical	Output filtering, human review
Code Deployment	Infrastructure	Sandboxing, rollback

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Integration Points

With Existing Frameworks

If You Use	Integration Point
LangChain	Add verification as chain step, use callbacks for monitoring
OpenAI Functions	Wrap function calls with permission checks
Anthropic Claude	Use system prompts for least-context, implement tool restrictions
AutoGPT-style	Add checkpoints between action cycles, implement kill switches

With CI/CD

Suggestion: Add safety layer tests to your pipeline
- Unit tests for output filters
- Integration tests for escalation paths
- Chaos testing for failure modes

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Debugging Safety Layers

Common Issues

Symptom	Likely Cause	Fix
Too many escalations	Thresholds too tight	Adjust based on false positive rate
Missed harmful outputs	Filter gaps	Review missed cases, expand rules
Slow responses	Heavy verification	Add caching, async verification
Verification false positives	Overfitting to training data	Diversify verifier inputs

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What Engineers Often Skip (But Shouldn’t)

1. Entanglements — If you use the same model in multiple places, failures correlate. Your 3 independent 90% verifiers might only give you 92%, not 99.9%.

2. Insurer’s Dilemma — If you set the risk budget but users bear the harm, you’ll systematically underestimate acceptable risk.

3. Capability Probing — Systems can hide capabilities. Periodic probing detects capability drift before it causes harm.

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See Also

• Quick Reference — Formulas at a glance
• Examples Catalog — All worked examples
• Composing Patterns — Combining patterns
• Common Mistakes — What to avoid