# Tractatus AI Safety Framework | Architectural Constraints for Human Agency
World's first production implementation of architectural AI safety constraints. Preserving human agency through structural, not aspirational, guarantees.

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## A Starting Point

Instead of hoping AI systems "behave correctly," we propose structural constraints where certain decision types require human judgment. These architectural boundaries can adapt to individual, organizational, and societal norms—creating a foundation for bounded AI operation that may scale more safely with capability growth.

We recognize this is one small step in addressing AI safety challenges. Explore the framework through the lens that resonates with your work.

For AI safety researchers, academics, and scientists investigating LLM failure modes and governance architectures

### Researcher

Academic & technical depth

Explore the theoretical foundations, architectural constraints, and scholarly context of the Tractatus framework.

- Technical specifications & proofs
- Academic research review
- Failure mode analysis
- Mathematical foundations

Explore Research
For software engineers, ML engineers, and technical teams building production AI systems

### Implementer

Code & integration guides

Get hands-on with implementation guides, API documentation, and reference code examples.

- Working code examples
- API integration patterns
- Service architecture diagrams
- Deployment best practices

View Implementation Guide
For AI executives, research directors, startup founders, and strategic decision makers setting AI safety policy

### Leader

Strategic AI Safety

Navigate the business case, compliance requirements, and competitive advantages of structural AI safety.

- Executive briefing & business case
- Risk management & compliance (EU AI Act)
- Implementation roadmap & ROI
- Competitive advantage analysis

View Leadership Resources

## Framework Capabilities

### Instruction Classification

Quadrant-based classification (STR/OPS/TAC/SYS/STO) with time-persistence metadata tagging

### Cross-Reference Validation

Validates AI actions against explicit user instructions to prevent pattern-based overrides

### Boundary Enforcement

Implements Tractatus 12.1-12.7 boundaries - values decisions architecturally require humans

### Pressure Monitoring

Detects degraded operating conditions (token pressure, errors, complexity) and adjusts verification

### Metacognitive Verification

AI self-checks alignment, coherence, safety before execution - structural pause-and-verify

### Pluralistic Deliberation

Multi-stakeholder values deliberation without hierarchy - facilitates human decision-making for incommensurable values

## Real-World Validation

Framework validated in 6-month deployment across ~500 sessions with Claude Code

Pattern Bias Incident Interactive Demo

### The 27027 Incident

Real production incident where Claude Code defaulted to port 27017 (training pattern) despite explicit user instruction to use port 27027. CrossReferenceValidator detected the conflict and blocked execution—demonstrating how pattern recognition can override instructions under context pressure.

Why this matters: This failure mode gets worse as models improve—stronger pattern recognition means stronger override tendency. Architectural constraints remain necessary regardless of capability level.

View Interactive Demo

Additional case studies and research findings documented in technical papers

Browse Case Studies →