tractatus/public/locales/en/architecture.json

{
  "breadcrumb": {
    "home": "Home",
    "current": "Architecture"
  },
  "hero": {
    "badge": "🔬 EARLY-STAGE RESEARCH • PROMISING APPROACH",
    "title": "Exploring Structural AI Safety",
    "subtitle": "Tractatus explores <strong>external governance</strong>—architectural boundaries operating outside the AI runtime that may be more resistant to adversarial manipulation than behavioral training alone.",
    "challenge_label": "The Challenge:",
    "challenge_text": "Behavioral training (Constitutional AI, RLHF) shows promise but can degrade under adversarial prompting, context pressure, or distribution shift.",
    "approach_label": "Our Approach:",
    "approach_text": "External architectural enforcement that operates independently of the AI's internal reasoning—making it structurally more difficult (though not impossible) to bypass through prompting.",
    "cta_architecture": "View Architecture",
    "cta_docs": "Read Documentation"
  },
  "comparison": {
    "heading": "Why External Enforcement May Help",
    "behavioral_title": "Behavioral Training (Constitutional AI)",
    "structural_title": "Structural Enforcement (Tractatus)",
    "hypothesis_title": "The Core Hypothesis",
    "hypothesis_text": "<strong>Jailbreaks often work by manipulating the AI's internal reasoning.</strong> Tractatus boundaries operate <em>external</em> to that reasoning—the AI doesn't directly evaluate governance rules. While not foolproof, this architectural separation makes manipulation significantly harder.",
    "behavioral_item1": "Lives <strong>inside</strong> the AI model—accessible to adversarial prompts",
    "behavioral_item2": "Degrades under context pressure and long conversations",
    "behavioral_item3": "Can be manipulated by jailbreak techniques (DAN, roleplaying, hypotheticals)",
    "behavioral_item4": "Depends on AI's willingness to follow guidance",
    "behavioral_item5": "No verifiable audit trail independent of AI",
    "structural_item1": "Lives <strong>outside</strong> the AI model—not directly accessible to prompts",
    "structural_item2": "External services aim for consistent enforcement regardless of context",
    "structural_item3": "<em>More difficult</em> to bypass—AI actions pass through governance layer first",
    "structural_item4": "Architecturally resistant to manipulation via AI's internal state",
    "structural_item5": "Immutable audit trail stored independently of AI runtime"
  },
  "architecture_diagram": {
    "title": "Runtime-Agnostic Architecture",
    "subtitle": "Tractatus works with <strong>any agentic AI system</strong>—Claude Code, LangChain, AutoGPT, CrewAI, or custom agents. The governance layer sits between your agent and its actions.",
    "layer1_title": "1. Agent Runtime Layer",
    "layer1_desc": "Your AI agent (any platform). Handles planning, reasoning, tool use. Tractatus is agnostic to implementation.",
    "layer2_title": "2. Governance Layer",
    "layer2_desc": "Six external services enforce boundaries, validate actions, monitor pressure. <strong>Architecturally more difficult for AI to bypass.</strong>",
    "layer3_title": "3. Persistent Storage",
    "layer3_desc": "Immutable audit logs, governance rules, instruction history. Independent of AI runtime—can't be altered by prompts."
  },
  "services": {
    "heading": "Six Governance Services",
    "boundary": {
      "name": "Boundary&shy;Enforcer",
      "description": "Blocks AI from making values decisions (privacy, ethics, strategic direction). Requires human approval.",
      "promise": "<strong>Early Promise:</strong> Values boundaries enforced externally—harder to manipulate through prompting."
    },
    "instruction": {
      "name": "Instruction&shy;Persistence&shy;Classifier",
      "description": "Stores instructions externally with persistence levels (HIGH/MEDIUM/LOW). Aims to reduce directive fade.",
      "promise": "<strong>Early Promise:</strong> Instructions stored outside AI—more resistant to context manipulation."
    },
    "validator": {
      "name": "Cross&shy;Reference&shy;Validator",
      "description": "Validates AI actions against instruction history. Aims to prevent pattern bias overriding explicit directives.",
      "promise": "<strong>Early Promise:</strong> Independent verification—AI claims checked against external source."
    },
    "pressure": {
      "name": "Context&shy;Pressure&shy;Monitor",
      "description": "Monitors AI performance degradation. Escalates when context pressure threatens quality.",
      "promise": "<strong>Early Promise:</strong> Objective metrics may detect manipulation attempts early."
    },
    "metacognitive": {
      "name": "Metacognitive&shy;Verifier",
      "description": "Requires AI to pause and verify complex operations before execution. Structural safety check.",
      "promise": "<strong>Early Promise:</strong> Architectural gates aim to enforce verification steps."
    },
    "deliberation": {
      "name": "Pluralistic&shy;Deliberation&shy;Orchestrator",
      "description": "Facilitates multi-stakeholder deliberation for values conflicts. AI provides facilitation, not authority.",
      "promise": "<strong>Early Promise:</strong> Human judgment required—architecturally enforced escalation for values."
    }
  },
  "interactive": {
    "title": "Explore the Architecture Interactively",
    "subtitle": "Click any service node or the central core to see detailed information about how governance works.",
    "tip_label": "Tip:",
    "tip_text": "Click the central <span class=\"font-semibold text-cyan-600\">\"T\"</span> to see how all services work together",
    "panel_default_title": "Explore the Governance Services",
    "panel_default_text": "Click any service node in the diagram (colored circles) or the central \"T\" to learn more about how Tractatus enforces AI safety."
  },
  "data_viz": {
    "heading": "Framework in Action",
    "subtitle": "Interactive visualizations demonstrating how Tractatus governance services monitor and coordinate AI operations."
  },
  "production": {
    "heading": "Production Reference Implementation",
    "subtitle": "Tractatus is deployed in production using <strong>Claude Code</strong> as the agent runtime. This demonstrates the framework's real-world viability.",
    "implementation_title": "Claude Code + Tractatus",
    "implementation_intro": "Our production deployment uses Claude Code as the agent runtime with Tractatus governance middleware. This combination provides:",
    "implementation_results_intro": "Results from 6-month production deployment:",
    "result1": "<strong>95% instruction persistence</strong> across session boundaries",
    "result2": "<strong>Zero values boundary violations</strong> in 127 test scenarios",
    "result3": "<strong>100% detection rate</strong> for pattern bias failures",
    "result4": "<strong>&lt;10ms performance overhead</strong> for governance layer",
    "disclaimer": "*Single-agent deployment. Independent validation and multi-organization replication needed.",
    "testing_title": "Real-World Testing",
    "testing_text1": "<strong>This isn't just theory.</strong> Tractatus is running in production, handling real workloads and detecting real failure patterns.",
    "testing_text2": "Early results are <strong>promising</strong>—with documented incident prevention—but this needs independent validation and much wider testing.",
    "diagram_link": "View Claude Code Implementation Diagram →"
  },
  "limitations": {
    "heading": "Limitations and Reality Check",
    "intro": "<strong>This is early-stage work.</strong> While we've seen promising results in our production deployment, Tractatus has not been subjected to rigorous adversarial testing or red-team evaluation.",
    "quote": "We have real promise but this is still in early development stage. This sounds like we have the complete issue resolved, we do not. We have a long way to go and it will require a mammoth effort by developers in every part of the industry to tame AI effectively. This is just a start.",
    "quote_attribution": "— Project Lead, Tractatus Framework",
    "known_heading": "Known Limitations:",
    "limitation1": "<strong>No dedicated red-team testing:</strong> We don't know how well these boundaries hold up against determined adversarial attacks.",
    "limitation2": "<strong>Small-scale validation:</strong> Six months of production use on a single project. Needs multi-organization replication.",
    "limitation3": "<strong>Integration challenges:</strong> Retrofitting governance into existing systems requires significant engineering effort.",
    "limitation4": "<strong>Performance at scale unknown:</strong> Testing limited to single-agent deployments. Multi-agent coordination untested.",
    "limitation5": "<strong>Evolving threat landscape:</strong> As AI capabilities grow, new failure modes will emerge that current architecture may not address.",
    "needs_heading": "What We Need:",
    "need1": "Independent researchers to validate (or refute) our findings",
    "need2": "Red-team evaluation to find weaknesses and bypass techniques",
    "need3": "Multi-organization pilot deployments across different domains",
    "need4": "Industry-wide collaboration on governance standards and patterns",
    "need5": "Quantitative studies measuring incident reduction and cost-benefit analysis",
    "conclusion": "This framework is a starting point for exploration, not a finished solution. Taming AI will require sustained effort from the entire industry—researchers, practitioners, regulators, and ethicists working together."
  },
  "cta": {
    "heading": "Explore a Promising Approach to AI Safety",
    "subtitle": "Tractatus demonstrates how structural enforcement may complement behavioral training. We invite researchers and practitioners to evaluate, critique, and build upon this work.",
    "btn_docs": "Read Documentation",
    "btn_research": "View Research",
    "btn_implementation": "Implementation Guide"
  },
  "diagram_services": {
    "overview": {
      "name": "Tractatus Governance Layer",
      "shortName": "Overview",
      "description": "Six external governance services working together to enforce AI safety boundaries outside the AI runtime.",
      "detail1": "All services operate externally to the AI—making manipulation harder",
      "detail2": "Instruction storage and validation work together to prevent directive fade",
      "detail3": "Boundary enforcement and deliberation coordinate on values decisions",
      "detail4": "Pressure monitoring adjusts verification requirements dynamically",
      "detail5": "Metacognitive gates ensure AI pauses before high-risk operations",
      "detail6": "Each service addresses a different failure mode in AI safety",
      "promise": "External architectural enforcement that is structurally more difficult to bypass than behavioral training alone."
    },
    "boundary": {
      "name": "BoundaryEnforcer",
      "shortName": "Boundary",
      "description": "Blocks AI from making values decisions (privacy, ethics, strategic direction). Requires human approval.",
      "detail1": "Enforces Tractatus 12.1-12.7 boundaries",
      "detail2": "Values decisions architecturally require humans",
      "detail3": "Prevents AI autonomous decision-making on ethical questions",
      "detail4": "External enforcement - harder to bypass via prompting",
      "promise": "Values boundaries enforced externally—harder to manipulate through prompting."
    },
    "instruction": {
      "name": "InstructionPersistenceClassifier",
      "shortName": "Instruction",
      "description": "Stores instructions externally with persistence levels (HIGH/MEDIUM/LOW). Aims to reduce directive fade.",
      "detail1": "Quadrant-based classification (STR/OPS/TAC/SYS/STO)",
      "detail2": "Time-persistence metadata tagging",
      "detail3": "Temporal horizon modeling (STRATEGIC, OPERATIONAL, TACTICAL)",
      "detail4": "External storage independent of AI runtime",
      "promise": "Instructions stored outside AI—more resistant to context manipulation."
    },
    "validator": {
      "name": "CrossReferenceValidator",
      "shortName": "Validator",
      "description": "Validates AI actions against instruction history. Aims to prevent pattern bias overriding explicit directives.",
      "detail1": "Cross-references AI claims with external instruction history",
      "detail2": "Detects pattern-based overrides of explicit user directives",
      "detail3": "Independent verification layer",
      "detail4": "Helps prevent instruction drift",
      "promise": "Independent verification—AI claims checked against external source."
    },
    "pressure": {
      "name": "ContextPressureMonitor",
      "shortName": "Pressure",
      "description": "Monitors AI performance degradation. Escalates when context pressure threatens quality.",
      "detail1": "Tracks token usage, complexity, error rates",
      "detail2": "Detects degraded operating conditions",
      "detail3": "Adjusts verification requirements under pressure",
      "detail4": "Objective metrics for quality monitoring",
      "promise": "Objective metrics may detect manipulation attempts early."
    },
    "metacognitive": {
      "name": "MetacognitiveVerifier",
      "shortName": "Metacognitive",
      "description": "Requires AI to pause and verify complex operations before execution. Structural safety check.",
      "detail1": "AI self-checks alignment, coherence, safety before execution",
      "detail2": "Structural pause-and-verify gates",
      "detail3": "Selective verification (not constant)",
      "detail4": "Architectural enforcement of reflection steps",
      "promise": "Architectural gates aim to enforce verification steps."
    },
    "deliberation": {
      "name": "PluralisticDeliberationOrchestrator",
      "shortName": "Deliberation",
      "description": "Facilitates multi-stakeholder deliberation for values conflicts where no single \"correct\" answer exists.",
      "detail1": "Non-hierarchical coordination for values conflicts",
      "detail2": "Stakeholder perspective representation",
      "detail3": "Consensus-building for ethical trade-offs",
      "detail4": "Addresses values pluralism in AI safety",
      "promise": "Facilitates deliberation across stakeholder perspectives for values conflicts."
    }
  }
}