DarkOps

DarkOps is an AI governance platform with a custom retrieval-augmented generation (RAG) system. Built before RAG was mainstream, it solves the "context bleeding" problem - when AI gets confused mixing PostgreSQL context into Redis tasks, or security patterns into performance optimization.

The platform uses a 10-layer context assembly architecture where each layer serves a specific purpose: base rules, domain knowledge, agent personality, invasiveness permissions, caution levels, specialized expertise, function patterns, field notes, and conversation history.

DarkOps implements enterprise AI governance through conditional tool provisioning - the first platform to enforce role-based AI capabilities. Junior staff get safe AI (Technician level - can read but not modify), senior architects get powerful AI (Operative level - can modify system intelligence).

• Conditional Tool Provisioning: Tools dynamically provided based on invasiveness level
• Server-Side Authorization: Technicians can't escalate privileges even if they try
• Audit Logging: Full change tracking for compliance (SOC2, HIPAA ready)
• Immediate Effect: Context updates propagate via 4-tier cache invalidation

• Dual-File Validation Workflow: Solves critical LLM truncation problem through programmatic validation. Despite 35,229 characters of explicit instructions, LLMs truncate large content 30-40% of the time with '[Rest remains same]' placeholders. Export creates locked reference + editable working copy, LLM performs native file operations, then validation compares both to detect truncation (>10% loss), corruption patterns, or structure damage. Zero truncation failures in production (100+ updates tested). Engineering solution to fundamental LLM limitation.
• Domain-Aware RAG with Retrieval Best Practices: Solves context bleeding through vector embeddings and domain-scoped retrieval. Domains chunked with overlap for context preservation. Field notes retrieved via hybrid search (semantic + keyword), filtered by domain scope with relevance thresholding (PostgreSQL tasks only get PostgreSQL context above quality cutoffs), deduplicated to remove redundant entries, then re-ranked by effectiveness scores. Multi-stage retrieval pipeline prevents AI confusion from irrelevant information.
• Agent Specialization Matrix: Two-dimensional framework (Invasiveness × Caution) creates precise capability matching. 17 base templates dynamically compose to generate 120 unique agent configurations, achieving 60-70% token savings vs monolithic prompts. Agents share base layers, only specialize deltas - surgical updates fix one template, 15 agents benefit instantly. Agents range from read-only Sentinels to system-modifying Infiltrators, with decision-making from cautious Quartermasters to fast-acting Ronins.
• Self-Improving Agents: Effectiveness scoring tracks which field notes produce successful outcomes. Adaptive learning thresholds adjust based on agent's learning velocity, enabling natural specialization emergence.
• Production Engineering Patterns: Circuit breaker pattern prevents cascade failures, 4-tier caching architecture (Service Layer → Repository Layer → Database Static Cache → Redis Distributed Cache) achieving 90%+ cache hit rate with 36-48ms average context assembly, transaction-aware cache invalidation, comprehensive audit logging.
• Multi-Layer Security: Template validation with regex blacklists, sandboxed execution, HMAC signatures, HTML sanitization, input size limits, pattern detection for injection attacks.
• Repository Pattern Architecture: Composition over inheritance with specialized repositories coordinating multiple base repositories. Clean separation with dictionary returns (not ORM objects) preventing session detachment.

Patterns & Practices

• Meta-Cognitive AI Architecture: Self-improving system with mission analyst loop ($0.02 per analysis vs $10k+ RLHF), layer-specific diagnostics, and fast feedback cycles
• Enterprise AI Governance: Conditional tool provisioning for role-based AI capabilities, server-side authorization preventing privilege escalation, SOC2/HIPAA-ready audit logging
• Custom RAG Implementation: Built retrieval-augmented generation before it was mainstream, implementing chunking with overlap, hybrid search (semantic + keyword), relevance thresholding, deduplication, domain-scoped filtering, and effectiveness-based re-ranking for multi-stage retrieval pipeline
• Production Engineering: 4-tier caching achieving 90%+ hit rate with 36-48ms context assembly, circuit breakers, graceful degradation, comprehensive error handling
• Security Engineering: Multi-layer validation, injection prevention, sandboxed execution, audit logging
• Distributed Systems: 4-layer architecture with clean separation, async operations, state management
• Platform Thinking: Flask blueprint pattern for embeddable integration, repository pattern for data access
• Technical Writing: 27,000+ lines of comprehensive documentation with metaphors and examples
• Systems Architecture: Designed for multi-tenancy, cross-organizational intelligence, platform evolution