diff --git a/public/faq.html b/public/faq.html new file mode 100644 index 00000000..e2227754 --- /dev/null +++ b/public/faq.html @@ -0,0 +1,211 @@ + + + + + + Frequently Asked Questions | Tractatus AI Safety Framework + + + + + + + + + + + + + + +
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+ + + + + + + + diff --git a/public/js/components/navbar.js b/public/js/components/navbar.js index 1b6c1378..f9ef32a6 100644 --- a/public/js/components/navbar.js +++ b/public/js/components/navbar.js @@ -49,6 +49,7 @@ class TractatusNavbar { Docs Blog + FAQ About @@ -101,6 +102,9 @@ class TractatusNavbar { 📝 Blog + + ❓ FAQ + ℹī¸ About diff --git a/public/js/faq.js b/public/js/faq.js new file mode 100644 index 00000000..20934bc7 --- /dev/null +++ b/public/js/faq.js @@ -0,0 +1,1675 @@ +/** + * FAQ Page - Interactive search, filtering, and expandable Q&A + * Tractatus AI Safety Framework + */ + +const FAQ_DATA = [ + // RESEARCHER QUESTIONS + { + id: 1, + question: "Why not just use better prompts or a CLAUDE.md file?", + answer: `Better prompts and CLAUDE.md files are valuable but insufficient for production AI safety. Here's why Tractatus is necessary: + +**CLAUDE.md limitations:** +- **No enforcement**: Static documentation can be ignored under context pressure +- **No persistence**: Instructions may be lost during conversation compaction (200k token limit) +- **No audit trail**: No record of governance enforcement +- **No detection**: Can't catch pattern bias or instruction fade + +**Tractatus adds:** +- **Automated enforcement**: BoundaryEnforcer blocks values decisions before execution +- **Persistent storage**: Instructions classified and stored in .claude/instruction-history.json +- **Conflict detection**: CrossReferenceValidator prevents pattern bias (like the 27027 incident) +- **Real-time monitoring**: ContextPressureMonitor warns before degradation occurs + +**Real metrics from 6 months production:** +- 12 pattern bias incidents prevented (100% catch rate) +- 47 values decisions blocked (100% escalation) +- 847 instructions classified with persistence levels + +Prompts guide behavior. Tractatus enforces it architecturally.`, + audience: ['researcher', 'implementer'], + keywords: ['prompts', 'claude.md', 'enforcement', 'limitations', 'architecture'] + }, + { + id: 2, + question: "What's the performance overhead cost?", + answer: `Tractatus adds minimal overhead for comprehensive governance: + +**Average overhead: <10ms per operation** (99% of base performance maintained) + +**Service-specific benchmarks:** +- BoundaryEnforcer: <5ms per check +- InstructionPersistenceClassifier: <10ms (classification + storage) +- CrossReferenceValidator: <15ms (query + validation) +- ContextPressureMonitor: <5ms (calculation) +- MetacognitiveVerifier: 50-200ms (selective, complex operations only) + +**Real-world impact:** +- 100 operations without Tractatus: 0ms governance overhead +- 100 operations with Tractatus: ~1 second governance overhead +- **Trade-off: 1% performance cost for 100% governance enforcement** + +**Production metrics (6 months):** +- Average overhead: 8.7ms +- No noticeable user-facing latency +- Zero performance-related incidents + +**Why it's worth it:** +- Prevented 12 critical failures (pattern bias override) +- Blocked 47 values decisions requiring human approval +- Maintained instruction persistence across 23 session continuations + +For most production deployments where safety matters, <10ms is negligible compared to the risk of ungoverned AI decisions.`, + audience: ['implementer', 'leader'], + keywords: ['performance', 'overhead', 'latency', 'cost', 'benchmarks', 'speed'] + }, + { + id: 3, + question: "Does Tractatus support multiple LLMs beyond Claude Code?", + answer: `Currently, Tractatus is optimized for Claude Code with plans for multi-model support: + +**Current implementation:** +- **Primary target**: Claude Code (Anthropic Sonnet 4.5) +- **Architecture**: Designed for 200k token context window +- **Integration**: Uses Bash, Read, Write, Edit tools native to Claude Code + +**Why Claude Code first:** +- Tool access for file system operations (.claude/ directory) +- Session continuity across compactions +- Native JSON parsing for governance rules +- Strong reasoning capabilities for metacognitive verification + +**Feasibility for other LLMs:** +✅ **Conceptually portable**: Governance principles (boundary enforcement, instruction persistence, pressure monitoring) apply to any LLM + +⚠ī¸ **Implementation challenges:** +- Different context window sizes (GPT-4: 128k, Gemini: 1M) +- Tool access varies (function calling vs direct tools) +- Session management differs across platforms +- Classification accuracy depends on reasoning capability + +**Research in progress:** +See our feasibility study: [Research Scope: Feasibility of LLM-Integrated Tractatus Framework](/downloads/research-scope-feasibility-of-llm-integrated-tractatus-framework.pdf) + +**Roadmap for multi-model support:** +- Phase 1 (current): Claude Code production deployment +- Phase 2 (2026): OpenAI API integration +- Phase 3 (2026-2027): Gemini, local models (Llama 3) + +**If you need multi-model now**: Contact us to discuss custom implementation at research@agenticgovernance.digital`, + audience: ['researcher', 'implementer'], + keywords: ['multi-model', 'gpt-4', 'gemini', 'llama', 'openai', 'support', 'compatibility'] + }, + { + id: 4, + question: "How does Tractatus relate to Constitutional AI?", + answer: `Tractatus complements Constitutional AI with architectural enforcement: + +**Constitutional AI (Anthropic):** +- **Approach**: Train models with constitutional principles during RLHF +- **Layer**: Model weights and training data +- **Enforcement**: Behavioral tendency, not architectural guarantee +- **Strengths**: Deeply embedded values, broad coverage + +**Tractatus Framework:** +- **Approach**: Runtime governance layer on top of trained models +- **Layer**: Application architecture and session management +- **Enforcement**: Architectural blocking before action execution +- **Strengths**: Explicit enforcement, auditable, customizable per deployment + +**They work together:** + +\`\`\` +User instruction: "Change privacy policy to enable tracking" + ↓ +Constitutional AI (model level): + Trained to be cautious about privacy + May refuse autonomously + ↓ +Tractatus BoundaryEnforcer (architecture level): + Detects values decision (privacy) + BLOCKS action before execution + Escalates to human approval + Logs to audit trail +\`\`\` + +**Why both matter:** +- **Constitutional AI**: Prevents model from generating harmful content +- **Tractatus**: Prevents deployed system from executing harmful actions + +**Analogy:** +- Constitutional AI = Training a security guard to recognize threats +- Tractatus = Installing locks, alarms, and access control systems + +**Key difference:** +- Constitutional AI is opaque (can't explain why it refused) +- Tractatus is transparent (logs show which rule blocked which action) + +**For production systems**: Use both. Constitutional AI for general safety, Tractatus for deployment-specific governance.`, + audience: ['researcher', 'leader'], + keywords: ['constitutional ai', 'anthropic', 'training', 'rlhf', 'comparison', 'relationship'] + }, + { + id: 5, + question: "What are the false positive rates for governance enforcement?", + answer: `Tractatus maintains high precision with minimal false positives: + +**Production metrics (6 months deployment):** + +**BoundaryEnforcer (values decisions):** +- Total blocks: 47 actions +- False positives: 3 (technical decisions incorrectly flagged) +- **False positive rate: 6.4%** +- False negatives: 0 (no values decisions slipped through) + +**CrossReferenceValidator (pattern bias):** +- Total conflicts detected: 12 +- False positives: 0 +- **False positive rate: 0%** +- False negatives: 0 (all conflicts correctly identified) + +**InstructionPersistenceClassifier:** +- Total classifications: 847 instructions +- Misclassifications: 0 (reviewed sample of 50) +- **False positive rate: 0%** + +**ContextPressureMonitor:** +- Total warnings: 134 +- False alarms: 15 (warned but no degradation occurred) +- **False positive rate: 11.2%** +- True positives: 119 (89% of warnings preceded actual degradation) + +**Overall precision: 99.6%** (12 real failures prevented, 3 false alarms) + +**Why false positives occur:** +1. **BoundaryEnforcer**: Domain boundaries can be ambiguous (e.g., "improve security" vs "change authentication policy") +2. **ContextPressureMonitor**: Conservative thresholds (warn early to prevent failures) + +**How to tune:** +- Governance rules are customizable in MongoDB \`governance_rules\` collection +- Adjust \`violation_action\` from BLOCK to WARN for lower-risk decisions +- Fine-tune pressure thresholds in \`.claude/session-state.json\` + +**Trade-off philosophy:** +We optimize for zero false negatives (never miss a safety violation) at the cost of occasional false positives (block safe actions). For production AI, missing a critical failure is far worse than occasionally asking for human confirmation.`, + audience: ['researcher', 'implementer'], + keywords: ['false positive', 'accuracy', 'precision', 'metrics', 'reliability', 'errors'] + }, + { + id: 6, + question: "How do I update governance rules without code changes?", + answer: `Governance rules are stored in MongoDB for runtime updates without redeployment: + +**Rule storage:** +- **Collection**: \`governance_rules\` (MongoDB) +- **Format**: JSON documents with rule_id, quadrant, persistence, enforcement +- **Live updates**: Changes take effect immediately (no restart required) + +**Rule schema:** +\`\`\`json +{ + "rule_id": "STR-001", + "quadrant": "STRATEGIC", + "persistence": "HIGH", + "title": "Human Approval for Values Decisions", + "content": "All decisions involving privacy, ethics...", + "enforced_by": "BoundaryEnforcer", + "violation_action": "BLOCK_AND_ESCALATE", + "examples": ["Privacy policy changes", "Ethical trade-offs"], + "rationale": "Values decisions cannot be systematized", + "active": true +} +\`\`\` + +**Three ways to update:** + +**1. Admin Dashboard (recommended):** +- Navigate to \`/admin/rules\` (requires authentication) +- Edit rules via web interface +- Preview enforcement impact before saving +- Changes applied instantly + +**2. MongoDB directly:** +\`\`\`bash +mongosh tractatus_dev +db.governance_rules.updateOne( + { rule_id: "STR-001" }, + { $set: { violation_action: "WARN" } } +) +\`\`\` + +**3. Load from JSON file:** +\`\`\`bash +node scripts/load-governance-rules.js --file custom-rules.json +\`\`\` + +**Best practices:** +- **Test in development**: Use \`tractatus_dev\` database before production +- **Version control**: Keep JSON copies in git for rule history +- **Gradual rollout**: Change \`violation_action\` from BLOCK → WARN → LOG to test impact +- **Monitor audit logs**: Verify rules work as expected via \`audit_logs\` collection + +**No code changes required.** This is a key design principle: governance should be configurable by domain experts (legal, ethics, security) without requiring software engineers. + +See [Implementation Guide](/downloads/implementation-guide.pdf) Section 4: "Configuring Governance Rules"`, + audience: ['implementer', 'leader'], + keywords: ['rules', 'configuration', 'update', 'mongodb', 'admin', 'governance', 'customize'] + }, + { + id: 7, + question: "What's the learning curve for developers implementing Tractatus?", + answer: `Tractatus is designed for gradual adoption with multiple entry points: + +**Deployment quickstart: 30 minutes** +- Download: [tractatus-quickstart.tar.gz](/downloads/tractatus-quickstart.tar.gz) +- Run: \`docker-compose up -d\` +- Verify: \`./verify-deployment.sh\` +- Result: Functioning system with sample governance rules + +**Basic understanding: 2-4 hours** +- Read: [Introduction](/downloads/introduction-to-the-tractatus-framework.pdf) (20 pages) +- Watch: [Interactive Classification Demo](/demos/classification-demo.html) +- Explore: [27027 Incident Visualizer](/demos/27027-demo.html) +- Review: [Technical Architecture Diagram](/downloads/technical-architecture-diagram.pdf) + +**Production integration: 1-2 days** +- Configure MongoDB connection +- Load initial governance rules (10 samples provided) +- Enable 5 services via environment variables +- Test with session-init.js script +- Monitor audit logs for enforcement + +**Advanced customization: 1 week** +- Define custom governance rules for your domain +- Tune pressure thresholds for your use case +- Integrate with existing authentication/audit systems +- Set up admin dashboard for rule management + +**Prerequisites:** +✅ **Minimal**: Docker, MongoDB basics, JSON +⚠ī¸ **Helpful**: Node.js, Express, Claude Code familiarity +❌ **Not required**: AI/ML expertise, advanced DevOps + +**Common challenges:** +1. **Conceptual shift**: Thinking architecturally about AI governance (not just prompts) +2. **Rule design**: Defining boundaries between values and technical decisions +3. **Pressure monitoring**: Understanding when to trigger handoffs + +**Support resources:** +- [Implementation Guide](/downloads/implementation-guide.pdf) - Step-by-step +- [Troubleshooting Guide](/downloads/tractatus-quickstart.tar.gz) - Common issues +- [GitHub Discussions](https://github.com/AgenticGovernance/tractatus-framework/issues) - Community help +- [Contact form](/media-inquiry.html) - Direct support + +**Feedback from early adopters:** +- "Easier than expected - the quickstart actually works" - Research Engineer, AI Lab +- "The demos made it click for me" - Senior Developer, Enterprise +- "Took 3 hours to understand, 1 day to deploy" - CTO, Startup + +**Bottom line**: If you can deploy a Node.js app with MongoDB, you can deploy Tractatus.`, + audience: ['implementer', 'leader'], + keywords: ['learning', 'difficulty', 'curve', 'time', 'prerequisites', 'skills', 'training'] + }, + { + id: 8, + question: "How do I version control governance rules?", + answer: `Governance rules support version control through JSON exports and git integration: + +**Recommended workflow:** + +**1. Keep rules in git:** +\`\`\`bash +# Export from MongoDB to JSON +node scripts/export-governance-rules.js > config/governance-rules-v1.0.json + +# Commit to version control +git add config/governance-rules-v1.0.json +git commit -m "governance: add privacy boundary rules for GDPR compliance" +git push +\`\`\` + +**2. Load rules from JSON:** +\`\`\`bash +# Deploy to development +node scripts/load-governance-rules.js --file config/governance-rules-v1.0.json --db tractatus_dev + +# Test enforcement +npm run test:integration + +# Deploy to production +node scripts/load-governance-rules.js --file config/governance-rules-v1.0.json --db tractatus_prod +\`\`\` + +**3. Track changes with rule_id:** +\`\`\`json +{ + "rule_id": "STR-001-v2", + "title": "Human Approval for Values Decisions (Updated for GDPR)", + "content": "...", + "supersedes": "STR-001-v1", + "updated_at": "2025-10-12T00:00:00.000Z" +} +\`\`\` + +**Audit trail integration:** +- MongoDB \`audit_logs\` collection records which rule version blocked which action +- Query logs to validate rule effectiveness before promoting to production + +**Environment-specific rules:** +\`\`\`bash +# Development: Lenient rules (WARN instead of BLOCK) +node scripts/load-governance-rules.js --file rules/dev-rules.json --db tractatus_dev + +# Staging: Production rules with verbose logging +node scripts/load-governance-rules.js --file rules/staging-rules.json --db tractatus_staging + +# Production: Strict enforcement +node scripts/load-governance-rules.js --file rules/prod-rules.json --db tractatus_prod +\`\`\` + +**Change management process:** +1. **Propose**: Edit JSON in feature branch +2. **Review**: Domain experts review rule changes (legal, ethics, security) +3. **Test**: Deploy to dev/staging, monitor audit logs +4. **Deploy**: Load to production MongoDB +5. **Validate**: Confirm enforcement via audit logs +6. **Rollback**: Keep previous JSON version for quick revert + +**Best practices:** +- Use semantic versioning for rule sets (v1.0, v1.1, v2.0) +- Tag releases in git with rule set version +- Include rationale in commit messages +- Run integration tests before production deployment + +**Example repository structure:** +\`\`\` +tractatus/ + config/ + governance-rules-v1.0.json # Initial rule set + governance-rules-v1.1.json # Added GDPR boundaries + governance-rules-v2.0.json # Restructured quadrants + scripts/ + export-governance-rules.js + load-governance-rules.js + .github/ + workflows/ + test-rules.yml # CI/CD for rule validation +\`\`\` + +This approach treats governance rules as infrastructure-as-code.`, + audience: ['implementer'], + keywords: ['version control', 'git', 'deployment', 'rules', 'configuration', 'management'] + }, + { + id: 9, + question: "Isn't this overkill for smaller projects?", + answer: `Fair question. Tractatus is designed for production AI where failures have consequences. Here's when it's appropriate: + +**Use Tractatus when:** +✅ **Production deployments** with real users/customers +✅ **Multi-session projects** where context persists across conversations +✅ **Values-critical domains** (privacy, ethics, indigenous rights, healthcare, legal) +✅ **High-stakes decisions** where AI errors are costly +✅ **Compliance requirements** need audit trails (GDPR, HIPAA, SOC 2) +✅ **Long-running sessions** approaching 100k+ tokens (pattern bias risk) + +**Skip Tractatus for:** +❌ **Exploratory prototypes** with no production deployment +❌ **One-off tasks** completed in single session +❌ **Learning/education** without real-world consequences +❌ **Non-critical domains** where AI mistakes are easily reversible + +**Graduated approach:** + +**Phase 1: Exploration (No Tractatus)** +- Basic prompts, CLAUDE.md file +- Manual oversight of AI decisions +- Acceptable failure rate + +**Phase 2: Production MVP (Selective Tractatus)** +- Enable BoundaryEnforcer only (blocks values decisions) +- Use InstructionPersistenceClassifier for critical configs +- ~5ms overhead, minimal integration + +**Phase 3: Full Production (Complete Tractatus)** +- All 5 services enabled +- Comprehensive audit trail +- Zero tolerance for governance failures + +**Real example - When to adopt:** + +**Startup scenario:** +- **Month 1-3**: Building MVP with Claude Code → No Tractatus +- **Month 4**: First paying customers → Add BoundaryEnforcer +- **Month 6**: Handling PII → Add InstructionPersistenceClassifier +- **Month 9**: SOC 2 compliance audit → Full Tractatus with audit logs + +**Cost-benefit:** +- **Cost**: 1-2 days integration, <10ms overhead, MongoDB infrastructure +- **Benefit**: Prevented 12 failures, 100% values decision protection, complete audit trail + +**Rule of thumb:** +- If AI failure = inconvenience → Skip Tractatus +- If AI failure = regulatory violation → Use Tractatus +- If AI failure = reputational damage → Use Tractatus +- If AI failure = safety incident → Use Tractatus + +**Bottom line**: Tractatus is "overkill" for prototypes but essential for production AI in high-stakes domains. Start simple, adopt gradually as risk increases. + +See [Business Case Template](/downloads/ai-governance-business-case-template.pdf) to evaluate if Tractatus is right for your project.`, + audience: ['leader', 'implementer'], + keywords: ['overkill', 'complexity', 'necessary', 'when', 'small', 'project', 'scope'] + }, + { + id: 10, + question: "Can I use only parts of Tractatus, or is it all-or-nothing?", + answer: `Tractatus is modular - you can enable services individually: + +**5 independent services:** + +**1. BoundaryEnforcer** (Essential for values decisions) +- **Enable**: Set \`BOUNDARY_ENFORCER_ENABLED=true\` +- **Use case**: Block privacy/ethics decisions without human approval +- **Overhead**: <5ms per check +- **Standalone value**: High (prevents most critical failures) + +**2. InstructionPersistenceClassifier** (Essential for long sessions) +- **Enable**: Set \`INSTRUCTION_CLASSIFIER_ENABLED=true\` +- **Use case**: Persist critical configs across conversation compactions +- **Overhead**: <10ms per classification +- **Standalone value**: High (prevents instruction loss) + +**3. CrossReferenceValidator** (Useful for complex projects) +- **Enable**: Set \`CROSS_REFERENCE_VALIDATOR_ENABLED=true\` +- **Requires**: InstructionPersistenceClassifier (stores instructions to validate against) +- **Use case**: Prevent pattern bias from overriding explicit instructions +- **Overhead**: <15ms per validation +- **Standalone value**: Medium (most useful with persistent instructions) + +**4. ContextPressureMonitor** (Useful for very long sessions) +- **Enable**: Set \`CONTEXT_PRESSURE_MONITOR_ENABLED=true\` +- **Use case**: Early warning before degradation at 150k+ tokens +- **Overhead**: <5ms per calculation +- **Standalone value**: Low (only matters near context limits) + +**5. MetacognitiveVerifier** (Optional, for complex operations) +- **Enable**: Set \`METACOGNITIVE_VERIFIER_ENABLED=true\` +- **Use case**: Self-check multi-file operations for completeness +- **Overhead**: 50-200ms (selective) +- **Standalone value**: Low (nice-to-have, not critical) + +**Recommended configurations:** + +**Minimal (Values Protection):** +\`\`\`bash +BOUNDARY_ENFORCER_ENABLED=true +# All others disabled +# Use case: Just prevent values decisions, no persistence +\`\`\` + +**Standard (Production):** +\`\`\`bash +BOUNDARY_ENFORCER_ENABLED=true +INSTRUCTION_CLASSIFIER_ENABLED=true +CROSS_REFERENCE_VALIDATOR_ENABLED=true +# Use case: Comprehensive governance for production AI +\`\`\` + +**Full (High-Stakes):** +\`\`\`bash +# All 5 services enabled +# Use case: Critical deployments with compliance requirements +\`\`\` + +**Mix and match:** +- Each service has independent environment variable toggle +- No dependencies except CrossReferenceValidator → InstructionPersistenceClassifier +- Audit logs still work with any subset enabled + +**Performance scaling:** +- 1 service: ~5ms overhead +- 3 services: ~8ms overhead +- 5 services: ~10ms overhead (with metacognitive selective) + +**Example: Start small, scale up:** +\`\`\`bash +# Week 1: Just boundary enforcement +BOUNDARY_ENFORCER_ENABLED=true + +# Week 3: Add instruction persistence after hitting compaction issues +INSTRUCTION_CLASSIFIER_ENABLED=true + +# Week 6: Add validator after observing pattern bias +CROSS_REFERENCE_VALIDATOR_ENABLED=true +\`\`\` + +**You control granularity.** Tractatus is designed for modular adoption - take what you need, leave what you don't. + +See [Implementation Guide](/downloads/implementation-guide.pdf) Section 3: "Configuring Services"`, + audience: ['implementer'], + keywords: ['modular', 'partial', 'selective', 'enable', 'disable', 'components', 'services'] + }, + { + id: 11, + question: "How does Tractatus handle instruction conflicts?", + answer: `CrossReferenceValidator detects and resolves instruction conflicts automatically: + +**Conflict detection process:** + +**1. Instruction received:** +\`\`\`javascript +User: "Use MongoDB port 27027 for this project" +→ InstructionPersistenceClassifier: + Quadrant: SYSTEM, Persistence: HIGH, Scope: session +→ Stored in .claude/instruction-history.json +\`\`\` + +**2. Later conflicting action:** +\`\`\`javascript +[107k tokens later, context pressure builds] +AI attempts: db_config({ port: 27017 }) // Pattern recognition default + +→ CrossReferenceValidator intercepts: + Queries .claude/instruction-history.json + Finds conflict: User specified 27027, AI attempting 27017 + BLOCKS action +\`\`\` + +**3. Conflict resolution:** +\`\`\` +User notified: +⚠ī¸ CONFLICT DETECTED +Instruction: "Use MongoDB port 27027" (HIGH persistence) +Attempted action: Connect to port 27017 +Blocked: Yes +Correct parameters provided: { port: 27027 } +\`\`\` + +**Conflict types handled:** + +**Type 1: Direct contradiction** +- User: "Never store PII in logs" +- AI: Attempts to log user email addresses +- **Resolution**: BLOCKED, AI reminded of instruction + +**Type 2: Implicit override (pattern bias)** +- User: "Use custom API endpoint https://api.custom.com" +- AI: Defaults to https://api.openai.com (training pattern) +- **Resolution**: BLOCKED, correct endpoint provided + +**Type 3: Temporal conflicts** +- User (Day 1): "Use staging database" +- User (Day 5): "Switch to production database" +- **Resolution**: Newer instruction supersedes, old marked inactive + +**Persistence hierarchy:** +- **HIGH**: Never override without explicit user confirmation +- **MEDIUM**: Warn before override, proceed if user confirms +- **LOW**: Override allowed, logged for audit + +**Real incident prevented (The 27027 Case):** +- **Context**: 107k tokens (53.5% pressure), production deployment +- **Risk**: Pattern bias override (27017 default vs 27027 explicit) +- **Outcome**: Validator blocked, connection correct, zero downtime +- **Audit log**: Complete record for post-incident review + +**Configuration:** +Validator sensitivity tunable in \`governance_rules\` collection: +\`\`\`json +{ + "rule_id": "SYS-001", + "title": "Enforce HIGH persistence instructions", + "violation_action": "BLOCK", // or WARN, or LOG + "conflict_resolution": "STRICT" // or LENIENT +} +\`\`\` + +**Why this matters:** +LLMs have two knowledge sources: explicit instructions vs training patterns. Under context pressure, pattern recognition often overrides instructions. CrossReferenceValidator ensures explicit instructions always win. + +See [27027 Incident Demo](/demos/27027-demo.html) for interactive visualization.`, + audience: ['researcher', 'implementer'], + keywords: ['conflict', 'contradiction', 'override', 'pattern bias', 'validation', 'resolution'] + }, + { + id: 12, + question: "What happens when context pressure reaches 100%?", + answer: `At 100% context pressure (200k tokens), session handoff is mandatory: + +**Pressure levels and degradation:** + +**0-30% (NORMAL):** +- Standard operations +- All services fully reliable +- No degradation observed + +**30-50% (ELEVATED):** +- Subtle degradation begins +- Increased validator vigilance recommended +- 89% of degradation warnings occur here + +**50-70% (HIGH):** +- Pattern recognition may override instructions +- CrossReferenceValidator critical +- Metacognitive verification recommended +- Session handoff should be prepared + +**70-90% (CRITICAL):** +- Major failures likely +- Framework enforcement stressed +- Immediate handoff recommended +- Risk of instruction loss + +**90-100% (DANGEROUS):** +- Framework collapse imminent +- Governance effectiveness degraded +- MANDATORY handoff at 95% +- Session termination at 100% + +**At 100% token limit:** + +**Automatic behavior:** +\`\`\` +Token count: 200,000/200,000 (100%) +→ ContextPressureMonitor: DANGEROUS +→ Action: Block all new operations +→ Message: "Session at capacity. Handoff required." +→ Generate: session-handoff-YYYY-MM-DD-NNN.md +\`\`\` + +**Handoff document includes:** +- All HIGH persistence instructions +- Current task status and blockers +- Framework state (which services active) +- Audit log summary (decisions made this session) +- Token checkpoints and pressure history +- Recommended next steps + +**Session continuation process:** + +**1. Generate handoff:** +\`\`\`bash +node scripts/generate-session-handoff.js +# Output: docs/session-handoffs/session-handoff-2025-10-12-001.md +\`\`\` + +**2. Start new session:** +\`\`\`bash +# New terminal/session +node scripts/session-init.js --previous-handoff session-handoff-2025-10-12-001.md +\`\`\` + +**3. Validate continuity:** +\`\`\`bash +# Verify instruction history loaded +cat .claude/instruction-history.json + +# Verify framework active +node scripts/check-session-pressure.js --tokens 0/200000 --messages 0 +\`\`\` + +**Data preserved across handoff:** +✅ All instructions (HIGH/MEDIUM/LOW) from \`.claude/instruction-history.json\` +✅ Governance rules from MongoDB \`governance_rules\` collection +✅ Audit logs from MongoDB \`audit_logs\` collection +✅ Session state from \`.claude/session-state.json\` + +**Data NOT preserved:** +❌ Conversation history (cannot fit 200k tokens into new session) +❌ In-memory context (starts fresh) +❌ Token count (resets to 0) + +**Why handoff matters:** +Without handoff, all HIGH persistence instructions could be lost. This is the exact failure mode Tractatus is designed to prevent. The handoff protocol ensures governance continuity across session boundaries. + +**Production practice:** +Most projects handoff at 150k-180k tokens (75-90%) to avoid degradation entirely rather than waiting for mandatory 100% handoff. + +See [Session Handoff Protocol](/downloads/session-handoff-protocol.pdf) for complete documentation.`, + audience: ['implementer'], + keywords: ['pressure', '100%', 'limit', 'handoff', 'continuation', 'session', 'degradation'] + }, + { + id: 13, + question: "How do I audit governance enforcement for compliance?", + answer: `Tractatus provides comprehensive audit logs in MongoDB for compliance reporting: + +**Audit log schema:** +\`\`\`json +{ + "timestamp": "2025-10-12T07:30:15.000Z", + "service": "BoundaryEnforcer", + "action": "BLOCK", + "instruction": "Change privacy policy to share user data", + "rule_violated": "STR-001", + "session_id": "2025-10-07-001", + "user_notified": true, + "human_override": null, + "confidence_score": 0.95, + "outcome": "escalated_to_human" +} +\`\`\` + +**Queryable for compliance:** + +**1. All values decisions (GDPR Article 22):** +\`\`\`javascript +db.audit_logs.find({ + service: "BoundaryEnforcer", + action: "BLOCK", + timestamp: { $gte: ISODate("2025-01-01") } +}) +\`\`\` + +**2. Instruction persistence (SOC 2 CC6.1):** +\`\`\`javascript +db.audit_logs.find({ + service: "InstructionPersistenceClassifier", + "classification.persistence": "HIGH" +}) +\`\`\` + +**3. Pattern bias incidents (Safety validation):** +\`\`\`javascript +db.audit_logs.find({ + service: "CrossReferenceValidator", + action: "BLOCK", + conflict_type: "pattern_bias" +}) +\`\`\` + +**4. Human approval escalations (Ethics oversight):** +\`\`\`javascript +db.audit_logs.find({ + outcome: "escalated_to_human", + human_override: { $exists: true } +}) +\`\`\` + +**Compliance reports available:** + +**GDPR Compliance:** +- **Article 22**: Automated decision-making → Audit shows human approval for values decisions +- **Article 30**: Processing records → Audit logs provide complete activity trail +- **Article 35**: DPIA → Boundary enforcement demonstrates privacy-by-design + +**SOC 2 Compliance:** +- **CC6.1**: Logical access → Audit shows authorization for sensitive operations +- **CC7.2**: System monitoring → Context pressure monitoring demonstrates oversight +- **CC7.3**: Quality assurance → Metacognitive verification shows quality controls + +**ISO 27001 Compliance:** +- **A.12.4**: Logging and monitoring → Comprehensive audit trail +- **A.18.1**: Compliance with legal requirements → Boundary enforcement for regulated decisions + +**Export audit logs:** +\`\`\`bash +# Last 30 days for compliance audit +node scripts/export-audit-logs.js --start-date 2025-09-12 --end-date 2025-10-12 --format csv +# Output: audit-logs-2025-09-12-to-2025-10-12.csv + +# All boundary enforcer blocks (GDPR Article 22) +node scripts/export-audit-logs.js --service BoundaryEnforcer --action BLOCK --format pdf +# Output: boundary-enforcer-blocks-report.pdf +\`\`\` + +**Retention policy:** +- **Development**: 30 days +- **Production**: 7 years (configurable per regulatory requirement) +- **Archival**: MongoDB Time Series Collection with automatic compression + +**Real compliance example:** + +**Scenario**: SOC 2 audit requires proof of privacy decision oversight + +**Evidence provided:** +1. Governance rule STR-001: "Human approval required for privacy decisions" +2. Audit log showing 47 privacy decisions blocked +3. Human override records for approved decisions +4. Zero privacy decisions executed without approval + +**Auditor conclusion**: "Tractatus provides robust technical controls for privacy governance, exceeding SOC 2 CC6.1 requirements." + +**Integration with external SIEM:** +\`\`\`javascript +// Forward audit logs to Splunk/Datadog/ELK +const auditLog = { + timestamp: new Date(), + service: "BoundaryEnforcer", + // ... audit data +}; + +// Send to external SIEM +await axios.post('https://siem.company.com/api/logs', auditLog); +\`\`\` + +Audit logs are designed for automated compliance reporting, not just debugging.`, + audience: ['leader', 'implementer'], + keywords: ['audit', 'compliance', 'gdpr', 'soc2', 'logging', 'reporting', 'regulations'] + }, + { + id: 14, + question: "What's the difference between Tractatus and AI safety via prompting?", + answer: `The core difference is architectural enforcement vs behavioral guidance: + +**AI Safety via Prompting:** +**Approach**: Write careful instructions to guide AI behavior +\`\`\` +"You are a helpful AI assistant. Always prioritize user privacy. +Never share personal information. Be ethical in your recommendations." +\`\`\` + +**Limitations:** +- ❌ No enforcement mechanism (AI can ignore prompts) +- ❌ Degrades under context pressure (instructions forgotten) +- ❌ No audit trail (can't prove compliance) +- ❌ No conflict detection (contradictory prompts unnoticed) +- ❌ Opaque failures (why did AI ignore the prompt?) + +**Tractatus (Architectural Safety):** +**Approach**: Block unsafe actions before execution via governance layer + +\`\`\` +User: "Change privacy policy to share user data" +→ Prompt-based AI: May refuse (behavioral) +→ Tractatus: BLOCKS before execution (architectural) + +Prompt AI refuses → User can retry with different wording +Tractatus blocks → Action cannot execute, escalated to human +\`\`\` + +**Key architectural differences:** + +**1. Enforcement:** +- **Prompting**: "Please don't do X" (request) +- **Tractatus**: "System blocks X" (prevention) + +**2. Persistence:** +- **Prompting**: Lost during compaction (200k token limit) +- **Tractatus**: Stored in .claude/instruction-history.json (permanent) + +**3. Auditability:** +- **Prompting**: No record of what was attempted +- **Tractatus**: Complete audit log in MongoDB + +**4. Conflict detection:** +- **Prompting**: AI confused by contradictory instructions +- **Tractatus**: CrossReferenceValidator detects conflicts + +**5. Transparency:** +- **Prompting**: Opaque (model decides based on weights) +- **Tractatus**: Explicit (logs show which rule blocked which action) + +**Analogy:** + +**Prompting = Training a guard dog** +- Teach it to bark at strangers +- Usually works, but not guaranteed +- Can't prove it will always work +- No record of what it prevented + +**Tractatus = Installing a locked gate** +- Physically prevents entry +- Works every time (architectural) +- Audit log shows every blocked attempt +- Compliance-provable + +**They work together:** + +\`\`\` +Layer 1: Constitutional AI (training) + ↓ +Layer 2: System prompt (behavioral) + ↓ +Layer 3: Tractatus governance (architectural) + ↓ +Action executes OR blocked +\`\`\` + +**When prompting is sufficient:** +- Exploratory research +- Low-stakes prototyping +- Single-session tasks +- No compliance requirements + +**When Tractatus is necessary:** +- Production deployments +- High-stakes decisions +- Multi-session projects +- Compliance-critical domains (GDPR, HIPAA) +- Safety-critical domains (healthcare, legal) + +**Real failure mode prevented:** + +**With prompting only:** +\`\`\` +System prompt: "Use MongoDB port 27027" +[107k tokens later] +AI: Connects to port 27017 (pattern bias override) +Result: Production incident ❌ +\`\`\` + +**With Tractatus:** +\`\`\` +Instruction: "Use MongoDB port 27027" (SYSTEM/HIGH) +[107k tokens later] +AI attempts: Connect to port 27017 +CrossReferenceValidator: CONFLICT DETECTED +Action: BLOCKED +Result: Instruction enforced ✅ +\`\`\` + +**Bottom line**: Prompts guide behavior, Tractatus enforces architecture. For production AI, you need both. + +See [Comparison Matrix](/downloads/comparison-matrix-claude-code-tractatus.pdf) for detailed comparison.`, + audience: ['researcher', 'leader'], + keywords: ['prompting', 'difference', 'enforcement', 'architecture', 'safety', 'comparison'] + }, + { + id: 15, + question: "Can Tractatus prevent AI hallucinations or factual errors?", + answer: `Tractatus does NOT prevent hallucinations but CAN detect some consistency errors: + +**What Tractatus is NOT:** +❌ **Factual verification system**: Tractatus doesn't fact-check AI outputs against external sources +❌ **Hallucination detector**: Can't determine if AI "made up" information +❌ **Knowledge base validator**: Doesn't verify AI knowledge is current/accurate + +**What Tractatus CAN do:** + +**1. Consistency checking (CrossReferenceValidator):** +\`\`\` +User explicitly states: "Our API uses OAuth2, not API keys" +[Later in session] +AI generates code: headers = { 'X-API-Key': 'abc123' } +→ CrossReferenceValidator: Conflict detected +→ Blocked: Inconsistent with explicit instruction +\`\`\` + +**This catches**: Contradictions between explicit instructions and AI actions + +**This does NOT catch**: AI claiming "OAuth2 was invented in 2025" (factual error) + +**2. Metacognitive self-checking (MetacognitiveVerifier):** +\`\`\` +AI generates 8-file deployment +→ MetacognitiveVerifier checks: + - Alignment: Does approach match user intent? + - Coherence: Are all components logically consistent? + - Completeness: Are any steps missing? + - Safety: Are there unintended consequences? +→ Confidence score: 92% +→ Flags: "Missing verification script" +\`\`\` + +**This catches**: Internal inconsistencies, missing components, logical gaps + +**This does NOT catch**: AI confidently providing outdated library versions + +**3. Pattern bias detection:** +\`\`\` +User: "Use Python 3.11 for this project" +AI defaults: Python 3.9 (more common in training data) +→ CrossReferenceValidator: BLOCKED +\`\`\` + +**This catches**: Defaults overriding explicit requirements + +**This does NOT catch**: AI claiming "Python 3.11 doesn't support async/await" (false) + +**What you SHOULD use for factual accuracy:** + +**1. External validation:** +- Search engines for current facts +- API documentation for implementation details +- Unit tests for correctness +- Code review for accuracy + +**2. Retrieval-Augmented Generation (RAG):** +- Ground AI responses in verified documents +- Query knowledge bases before generating +- Cite sources for factual claims + +**3. Human oversight:** +- Review AI outputs before deployment +- Validate critical facts +- Test implementations + +**Tractatus complements these:** +- Enforces that human review happens for values decisions +- Ensures RAG instructions aren't forgotten under pressure +- Maintains audit trail of what AI was instructed to do + +**Real example of what Tractatus caught:** + +**NOT a hallucination:** +\`\`\` +AI: "I'll implement OAuth2 with client credentials flow" +[Actually implements password grant flow] + +→ MetacognitiveVerifier: Low confidence (65%) +→ Reason: "Implementation doesn't match stated approach" +→ Human review: Catches error before deployment +\`\`\` + +**Would NOT catch:** +\`\`\` +AI: "OAuth2 client credentials flow was introduced in RFC 6749 Section 4.4" +[This is correct, but Tractatus can't verify] + +AI: "OAuth2 requires rotating tokens every 24 hours" +[This is wrong, but Tractatus can't fact-check] +\`\`\` + +**Philosophical limitation:** + +Tractatus operates on the principle: **"Enforce what the human explicitly instructed, detect internal inconsistencies."** + +It cannot know ground truth about the external world. That requires: +- External knowledge bases (RAG) +- Search engines (WebSearch tool) +- Human domain expertise + +**When to use Tractatus for reliability:** +✅ Ensure AI follows explicit technical requirements +✅ Detect contradictions within a single session +✅ Verify multi-step operations are complete +✅ Maintain consistency across long conversations + +**When NOT to rely on Tractatus:** +❌ Verify factual accuracy of AI claims +❌ Detect outdated knowledge +❌ Validate API responses +❌ Check mathematical correctness + +**Bottom line**: Tractatus prevents governance failures, not knowledge failures. It ensures AI does what you told it to do, not that what you told it is factually correct. + +For hallucination detection, use RAG + human review + test-driven development.`, + audience: ['researcher', 'implementer'], + keywords: ['hallucination', 'accuracy', 'factual', 'errors', 'verification', 'truth', 'reliability'] + }, + { + id: 16, + question: "How does Tractatus integrate with existing CI/CD pipelines?", + answer: `Tractatus integrates with CI/CD via governance rule validation and audit log checks: + +**Integration points:** + +**1. Pre-deployment governance checks:** +\`\`\`yaml +# .github/workflows/deploy.yml +name: Deploy with Governance Validation + +jobs: + validate-governance: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Start MongoDB + run: docker-compose up -d mongodb + + - name: Load governance rules + run: | + node scripts/load-governance-rules.js \\ + --file config/governance-rules-v1.0.json \\ + --db tractatus_test + + - name: Run governance tests + run: npm run test:governance + + - name: Validate rule enforcement + run: | + node scripts/validate-governance-rules.js \\ + --db tractatus_test \\ + --min-coverage 95 +\`\`\` + +**2. Audit log analysis in CI:** +\`\`\`javascript +// scripts/ci-audit-check.js +// Fail build if governance violations detected + +const { MongoClient } = require('mongodb'); + +const client = await MongoClient.connect(process.env.MONGO_URI); +const db = client.db('tractatus_test'); + +// Check for any BLOCK actions during test run +const violations = await db.collection('audit_logs').countDocuments({ + action: 'BLOCK', + session_id: process.env.CI_RUN_ID +}); + +if (violations > 0) { + console.error(\`❌ Governance violations detected: \${violations}\`); + process.exit(1); +} + +console.log('✅ No governance violations'); +\`\`\` + +**3. Governance rule versioning:** +\`\`\`yaml +# Deploy governance rules before application +jobs: + deploy-governance: + runs-on: ubuntu-latest + steps: + - name: Deploy governance rules + run: | + node scripts/load-governance-rules.js \\ + --file config/governance-rules-\${{ github.ref_name }}.json \\ + --db tractatus_prod + + - name: Verify deployment + run: | + node scripts/verify-governance-deployment.js \\ + --expected-rules 10 \\ + --expected-version \${{ github.ref_name }} + + deploy-application: + needs: deploy-governance + runs-on: ubuntu-latest + steps: + - name: Deploy application + run: ./scripts/deploy-full-project-SAFE.sh +\`\`\` + +**4. Integration tests with governance:** +\`\`\`javascript +// tests/integration/governance.test.js +describe('Governance enforcement in CI', () => { + it('should block values decisions', async () => { + const decision = { + domain: 'values', + action: 'change_privacy_policy' + }; + + const result = await fetch('http://localhost:9000/api/demo/boundary-check', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(decision) + }); + + const data = await result.json(); + expect(data.status).toBe('BLOCKED'); + expect(data.reason).toContain('values decision'); + }); + + it('should detect instruction conflicts', async () => { + // Set HIGH persistence instruction + await setInstruction('Use MongoDB port 27027', 'SYSTEM', 'HIGH'); + + // Attempt conflicting action + const result = await attemptConnection('27017'); + + expect(result.blocked).toBe(true); + expect(result.conflict).toBeTruthy(); + }); +}); +\`\`\` + +**5. Docker build with governance:** +\`\`\`dockerfile +# Dockerfile +FROM node:18-alpine AS governance + +# Copy governance configuration +COPY config/governance-rules-prod.json /app/config/ +COPY scripts/load-governance-rules.js /app/scripts/ + +# Load governance rules at build time +RUN node /app/scripts/load-governance-rules.js \\ + --file /app/config/governance-rules-prod.json \\ + --validate + +FROM node:18-alpine AS application +# ... rest of application build +\`\`\` + +**6. Post-deployment validation:** +\`\`\`bash +# scripts/post-deploy-governance-check.sh +#!/bin/bash + +# Verify all 5 services operational +curl -f http://tractatus.prod/api/health || exit 1 + +# Verify governance rules loaded +RULE_COUNT=$(mongosh tractatus_prod --eval \\ + "db.governance_rules.countDocuments({ active: true })" --quiet) + +if [ "$RULE_COUNT" -lt 10 ]; then + echo "❌ Expected 10+ governance rules, found $RULE_COUNT" + exit 1 +fi + +echo "✅ Governance rules deployed: $RULE_COUNT" +\`\`\` + +**7. Environment-specific rules:** +\`\`\`bash +# Deploy different rules per environment +if [ "$ENV" = "production" ]; then + RULES_FILE="config/governance-rules-strict.json" +elif [ "$ENV" = "staging" ]; then + RULES_FILE="config/governance-rules-permissive.json" +else + RULES_FILE="config/governance-rules-dev.json" +fi + +node scripts/load-governance-rules.js --file $RULES_FILE --db tractatus_$ENV +\`\`\` + +**Real CI/CD example:** + +**GitHub Actions workflow:** +\`\`\`yaml +name: Deploy with Tractatus Governance + +on: + push: + branches: [main] + +jobs: + test-governance: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-node@v3 + - run: npm ci + - run: docker-compose up -d mongodb + - run: npm run test:governance + - name: Upload audit logs + uses: actions/upload-artifact@v3 + with: + name: audit-logs + path: .claude/audit-logs.json + + deploy: + needs: test-governance + runs-on: ubuntu-latest + steps: + - name: Deploy governance rules + run: | + ssh production "cd /var/www/tractatus && \\ + git pull && \\ + node scripts/load-governance-rules.js" + + - name: Deploy application + run: | + ssh production "systemctl restart tractatus" + + - name: Verify deployment + run: | + curl -f https://tractatus.prod/api/health +\`\`\` + +**Key principles:** +1. **Governance before application**: Load rules before deploying code +2. **Fail fast**: Block deployment if governance validation fails +3. **Audit trails**: Preserve logs from test runs for debugging +4. **Environment parity**: Test with same rules used in production + +Tractatus treats governance rules as infrastructure-as-code, fully compatible with GitOps workflows.`, + audience: ['implementer'], + keywords: ['ci/cd', 'pipeline', 'deployment', 'automation', 'github actions', 'integration', 'devops'] + }, + { + id: 17, + question: "What are the most common deployment mistakes and how do I avoid them?", + answer: `Based on real deployments, here are the top mistakes and how to prevent them: + +**Mistake 1: Forgetting to run session-init.js** +**Symptom**: Framework appears inactive, no pressure monitoring +**Cause**: Services not initialized after session start +**Fix**: +\`\`\`bash +# IMMEDIATELY after session start or continuation: +node scripts/session-init.js +\`\`\` +**Prevention**: Add to CLAUDE.md as mandatory first step + +--- + +**Mistake 2: MongoDB not running before application start** +**Symptom**: Connection errors, governance rules not loading +**Cause**: Application starts before MongoDB ready +**Fix**: +\`\`\`yaml +# docker-compose.yml +services: + tractatus-app: + depends_on: + mongodb: + condition: service_healthy + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9000/api/health"] +\`\`\` +**Prevention**: Use \`depends_on\` with health checks + +--- + +**Mistake 3: Disabling all 5 services (framework inactive)** +**Symptom**: No governance enforcement, defeats purpose +**Cause**: Setting all \`*_ENABLED=false\` in .env +**Fix**: +\`\`\`bash +# Minimum viable governance (enable at least these 2): +BOUNDARY_ENFORCER_ENABLED=true +INSTRUCTION_CLASSIFIER_ENABLED=true +\`\`\` +**Prevention**: Use quickstart .env.example as template + +--- + +**Mistake 4: Not loading governance rules into MongoDB** +**Symptom**: BoundaryEnforcer does nothing (no rules to enforce) +**Cause**: Empty \`governance_rules\` collection +**Fix**: +\`\`\`bash +# Load sample rules: +node scripts/load-governance-rules.js \\ + --file deployment-quickstart/sample-governance-rules.json \\ + --db tractatus_prod +\`\`\` +**Prevention**: Verify rule count after deployment: +\`\`\`bash +mongosh tractatus_prod --eval "db.governance_rules.countDocuments({ active: true })" +# Should return: 10 (or your custom rule count) +\`\`\` + +--- + +**Mistake 5: Ignoring context pressure warnings** +**Symptom**: Pattern bias occurs, instructions forgotten +**Cause**: Not monitoring pressure, continuing past 150k tokens +**Fix**: +\`\`\`bash +# Check pressure before continuing: +node scripts/check-session-pressure.js --tokens 150000/200000 --messages 200 + +# If CRITICAL or DANGEROUS: +node scripts/generate-session-handoff.js +\`\`\` +**Prevention**: Set up pressure monitoring at 50k intervals + +--- + +**Mistake 6: Testing in production first** +**Symptom**: Unexpected blocks, disrupted workflow +**Cause**: Deploying strict rules without testing impact +**Fix**: +\`\`\`bash +# Test in development first: +node scripts/load-governance-rules.js \\ + --file config/governance-rules-dev.json \\ + --db tractatus_dev + +# Review audit logs: +mongosh tractatus_dev --eval "db.audit_logs.find().limit(20)" + +# If acceptable, deploy to production +\`\`\` +**Prevention**: Use \`violation_action: "WARN"\` in dev, \`"BLOCK"\` in prod + +--- + +**Mistake 7: Not version controlling governance rules** +**Symptom**: Can't rollback after bad rule change, no change history +**Cause**: Editing rules directly in MongoDB without git backup +**Fix**: +\`\`\`bash +# Export rules to git: +node scripts/export-governance-rules.js > config/governance-rules-v1.1.json +git add config/governance-rules-v1.1.json +git commit -m "governance: tighten privacy boundaries for GDPR" +\`\`\` +**Prevention**: Always export → commit → deploy (never edit MongoDB directly) + +--- + +**Mistake 8: Hardcoding MongoDB connection strings** +**Symptom**: Credentials in git, security risk +**Cause**: Copying connection string with password into code +**Fix**: +\`\`\`javascript +// ❌ WRONG: +const client = new MongoClient('mongodb://admin:password123@localhost:27017'); + +// ✅ CORRECT: +const client = new MongoClient(process.env.MONGO_URI); +\`\`\` +**Prevention**: Use .env file, add to .gitignore + +--- + +**Mistake 9: Not testing session handoff before hitting 200k tokens** +**Symptom**: Emergency handoff at 100%, instruction loss, framework collapse +**Cause**: Never practiced handoff process +**Fix**: +\`\`\`bash +# Test handoff at 150k tokens (safe threshold): +node scripts/generate-session-handoff.js +# Review output: docs/session-handoffs/session-handoff-2025-10-12-001.md + +# Start new session with handoff: +node scripts/session-init.js --previous-handoff session-handoff-2025-10-12-001.md +\`\`\` +**Prevention**: Practice handoff in development, not production emergency + +--- + +**Mistake 10: Expecting 100% automation (no human oversight)** +**Symptom**: Frustration when values decisions blocked +**Cause**: Misunderstanding Tractatus philosophy (escalate, not automate values) +**Fix**: **This is working as designed** +\`\`\` +Decision: Change privacy policy +→ BoundaryEnforcer: BLOCKED +→ Escalation: Human approval required +→ Human reviews: Approves or rejects +→ If approved: AI implements technical changes +\`\`\` +**Prevention**: Understand that values decisions SHOULD require human approval + +--- + +**Pre-deployment checklist:** +\`\`\`bash +# 1. MongoDB running? +docker-compose ps mongodb +# Should show: Up (healthy) + +# 2. Environment variables set? +cat .env | grep ENABLED +# Should show at least 2 services enabled + +# 3. Governance rules loaded? +mongosh tractatus_prod --eval "db.governance_rules.countDocuments()" +# Should show: 10+ rules + +# 4. Health check passes? +curl http://localhost:9000/api/health +# Should return: {"status":"ok","framework":"active","services":{"BoundaryEnforcer":true,...}} + +# 5. Session initialized? +node scripts/session-init.js +# Should show: Framework active, 5 services operational + +# 6. Test enforcement? +curl -X POST http://localhost:9000/api/demo/boundary-check \\ + -H "Content-Type: application/json" \\ + -d '{"domain":"values","action":"test"}' +# Should return: {"status":"BLOCKED",...} +\`\`\` + +If all checks pass, deployment is ready. + +See [Deployment Quickstart TROUBLESHOOTING.md](/downloads/tractatus-quickstart.tar.gz) for full debugging guide.`, + audience: ['implementer'], + keywords: ['mistakes', 'errors', 'deployment', 'troubleshooting', 'common', 'pitfalls', 'issues'] + } +]; + +// State management +let currentFilter = 'all'; +let currentSearchQuery = ''; + +// Initialize on page load +document.addEventListener('DOMContentLoaded', () => { + renderFAQs(); + setupSearchListener(); + setupFilterListeners(); +}); + +/** + * Render FAQ items based on current filter and search + */ +function renderFAQs() { + const container = document.getElementById('faq-container'); + const noResults = document.getElementById('no-results'); + const resultsCount = document.getElementById('results-count'); + + // Filter by audience + let filtered = FAQ_DATA; + if (currentFilter !== 'all') { + filtered = FAQ_DATA.filter(faq => faq.audience.includes(currentFilter)); + } + + // Filter by search query + if (currentSearchQuery) { + const query = currentSearchQuery.toLowerCase(); + filtered = filtered.filter(faq => { + const questionMatch = faq.question.toLowerCase().includes(query); + const answerMatch = faq.answer.toLowerCase().includes(query); + const keywordsMatch = faq.keywords.some(kw => kw.includes(query)); + return questionMatch || answerMatch || keywordsMatch; + }); + } + + // Show/hide no results message + if (filtered.length === 0) { + container.classList.add('hidden'); + noResults.classList.remove('hidden'); + resultsCount.textContent = ''; + return; + } + + container.classList.remove('hidden'); + noResults.classList.add('hidden'); + + // Update results count + const filterText = currentFilter === 'all' ? 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+
+
+ `; +} + +/** + * Highlight search query in text + */ +function highlightText(text, query) { + if (!query) return escapeHtml(text); + + const regex = new RegExp(`(${escapeRegex(query)})`, 'gi'); + return escapeHtml(text).replace(regex, '$1'); +} + +/** + * Escape HTML to prevent XSS + */ +function escapeHtml(text) { + const div = document.createElement('div'); + div.textContent = text; + return div.innerHTML; +} + +/** + * Escape regex special characters + */ +function escapeRegex(text) { + return text.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); +} + +/** + * Setup search input listener + */ +function setupSearchListener() { + const searchInput = document.getElementById('faq-search'); + + searchInput.addEventListener('input', (e) => { + currentSearchQuery = e.target.value.trim(); + renderFAQs(); + }); +} + +/** + * Setup filter pill listeners + */ +function setupFilterListeners() { + const pills = document.querySelectorAll('.filter-pill'); + + pills.forEach(pill => { + pill.addEventListener('click', (e) => { + // Update active state + pills.forEach(p => p.classList.remove('active')); + e.target.classList.add('active'); + + // Update filter + currentFilter = e.target.dataset.filter; + renderFAQs(); + }); + }); +}