AI MCP Gateway

# AI Orchestrator Notes ## Purpose This document tracks insights, patterns, and lessons learned from using the AI MCP Gateway to improve future performance and routing decisions. --- ## Initial Configuration (2024-11-21) ### Layer Structure - **Layer L0 (Free/Cheap)**: Mistral 7B Free, Qwen 2 7B Free, OSS Local models - **Layer L1 (Low-cost)**: Gemini Flash 1.5, GPT-4o Mini - **Layer L2 (Mid-tier)**: Claude 3 Haiku, GPT-4o - **Layer L3 (Premium)**: Claude 3.5 Sonnet, OpenAI o1 ### Default Routing Strategy 1. Start at L0 for most tasks 2. Enable cross-check for quality=high or quality=critical 3. Auto-escalate if conflicts detected 4. Max escalation to L2 by default (configurable) --- ## Lessons Learned ### Pattern 1: Simple tasks work well at L0 **Date**: TBD **Finding**: Simple refactoring, adding comments, basic code generation work well with free models **Action**: Continue defaulting to L0 for complexity=low ### Pattern 2: Code review benefits from cross-check **Date**: TBD **Finding**: Having 2+ models review code catches more bugs than single model **Action**: Always enable cross-check for quality=high tasks ### Pattern 3: Complex architecture needs L2+ **Date**: TBD **Finding**: System design, complex algorithms need reasoning capabilities of L2+ models **Action**: Auto-select L2 for complexity=high + quality=high --- ## Common Failure Modes ### Issue 1: Token limit exceeded **Symptom**: Error when processing very large files **Root cause**: Exceeding model context window **Solution**: Chunk large inputs, summarize context ### Issue 2: Inconsistent cross-check results **Symptom**: Models give contradictory answers on same task **Root cause**: Task ambiguity, lack of clear requirements **Solution**: Improve task decomposition, add clarifying questions --- ## Optimization Ideas 1. **Token estimation**: Improve accuracy of token counting for better cost prediction 2. **Model routing**: Add capability-based routing (e.g., prefer vision models for image tasks) 3. **Cache results**: Cache common patterns to reduce API calls 4. **Batch processing**: Group similar tasks to reduce overhead --- ## Appendix Add new entries above as you discover patterns and insights.