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MMA_Support draft

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Edward R. Gonzalez
2026-02-24 19:11:15 -05:00
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MMA_Support/Overview.md
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# 4-Tier Hierarchical Multi-Model Architecture (MMA) - Overview
# System Specification: 4-Tier Hierarchical Multi-Model Architecture
The 4-Tier Hierarchical Multi-Model Architecture is a conceptual framework designed to manage complexity in AI systems by decomposing responsibilities into distinct, specialized layers. This modular approach enhances scalability, maintainability, and overall system performance.
**Project:** `manual_slop` (or equivalent Agentic Co-Dev Prototype)
## Architectural Tiers
**Core Philosophy:** Token Economy, Strict Memory Siloing, and Human-In-The-Loop (HITL) Execution.
1. **Tier 1: User-Facing Model (The Orchestrator/Router)**
* Direct user interface and intent interpretation.
* Routes requests to appropriate specialized models or tools.
## 1. Architectural Overview
2. **Tier 2: Specialized Models (The Experts/Tools)**
* Domain-specific models or tools (e.g., code generation, data analysis).
* Performs the "heavy lifting" for specific tasks.
This system rejects the "monolithic black-box" approach to agentic coding. Instead of passing an entire codebase into a single expensive context window, the architecture mimics a senior engineering department. It uses a 4-Tier hierarchy where cognitive load and context are aggressively filtered from top to bottom.
3. **Tier 3: Data & Knowledge Base (The Information Layer)**
* A repository of structured and unstructured information.
* Provides context and facts to specialized models.
Expensive, high-reasoning models manage metadata and architecture (Tier 1 & 2), while cheap, fast models handle repetitive syntax and error parsing (Tier 3 & 4).
4. **Tier 4: Monitoring & Feedback (The Governance Layer)**
* Overarching layer for evaluation, error analysis, and continuous improvement.
* Closes the loop between user experience and model refinement.
### 1.1 Core Paradigms
## Core Goals
* **Modularity:** Decouple different functions to allow for independent development.
* **Efficiency:** Use smaller, specialized models for specific tasks instead of one monolithic model.
* **Contextual Accuracy:** Ensure specialized tools have access to relevant data.
* **Continuous Improvement:** Establish a systematic way to monitor performance and iterate.
* **Token Firewalling:** Error logs and deep history are never allowed to bubble up to high-tier models. The system relies heavily on abstracted AST views (Skeleton, Curated) rather than raw code when context allows.
* **Context Amnesia:** Worker agents (Tier 3) have their trial-and-error histories wiped upon task completion to prevent context ballooning and hallucination.
* **The Execution Clutch (HITL):** Agents operate based on Archetype Trust Scores defined in configuration. Trusted patterns run in `Auto` mode; untrusted or complex refactors run in `Step` mode, pausing before tool execution for human review and JSON history mutation.
* **Interface-Driven Development (IDD):** The architecture inherently prioritizes the creation of contracts (stubs, schemas) before implementation, allowing workers to proceed in parallel without breaking cross-module boundaries.