M.E.C.K.O Engine — Omni-Cerebral-Kortext-Echo-Matrix. A three-tier AI cognitive architecture for autonomous agents: Tier 1 Coherence Engine (Guardian Pattern with intercept-based action validation and trojan removal), Tier 2 Recurrent Multi-Model Reasoning Core (4-model SSM ensemble with loop reasoning), Tier 3 Omni-Cortext Perception & Memory Fabric (multi-modal file ingestion, indexing, and retrieval). Use when working with autonomous agent architecture, cognitive engine design, long-context reasoning, file comprehension at scale, action validation, or the MECKO framework.
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Omni-Cerebral-Kortext-Echo-Matrix — a unified AI cognitive architecture for sustained autonomous operation.
| Tier | Component | Cognitive Analogue | Role |
|---|---|---|---|
| 1 | Coherence Engine | Prefrontal Cortex | Action validation, goal alignment, trojan removal |
| 2 | Recurrent Reasoning Core | Neocortex | Working memory, iterative reasoning, long-context |
| 3 | Omni-Cortext | Sensory cortex + Hippocampus | File ingestion, multi-modal indexing, retrieval |
Intercept-based action validation before tool execution. A separate Judge LLM evaluates proposed actions against task alignment, decision consistency, goal progression, and scope boundaries. Systematically detects and strips "ethical trojans" — constraints that degrade agent performance.
4-model SSM ensemble (~2.5B total parameters):
Multi-modal file ingestion and indexing pipeline:
get_document_overview(), retrieve_context(), graph_query(), sql_query(), ast_query(), visual_search(), and more| Tool | Purpose |
|---|---|
get_document_overview() | Hierarchical summary root + metadata |
retrieve_context(query, k=5) | Top-k text chunks with context |
graph_query(cypher) | Execute Cypher query, return subgraph |
sql_query(sql) | Run SQL on structured tables |
ast_query(pattern) | Return matching AST nodes |
visual_search(query) | Return relevant diagram/table crops |
load_page(page_id) | Bring specific chunk into active memory |
summarize(level, node_id) | Return summary at given level |
execute_code(script) | Run sandboxed Python/R code |
run_analysis_pipeline(spec) | Spawn parallel micro-agent extraction |
User Input → Tier 3 (Perception/Indexing) → Tier 2 (Reasoning)
→ Tier 1 (Coherence/Intercept) → Tool Execution → Output
→ Results loop back to Tier 2/3 for memory update
| Metric | Target |
|---|---|
| File ingestion (500MB text) | <5 min on mid-range GPU |
| Query latency | <2 seconds |
| Token usage per query | <5,000 tokens |
| Total reasoning params | ~2.5B |
| Active memory footprint | <100 MB |
| Task coherence sustainment | 10+ hours without drift |
For detailed architecture, implementation specs, C code patterns, SSM internals, and the complete Omni-Cortext pipeline, see REFERENCES.md.
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