Overview
Compass is a sophisticated AI productivity system that leverages multiple domain-specific agents to handle complex tasks. The system uses a custom Model Context Protocol (MCP) server to orchestrate communication between agents, enabling seamless collaboration on multi-step workflows. Each agent is specialized for specific domains—research, coding, analysis, and communication—allowing for expert-level performance across diverse tasks.
Architecture
The system follows a hub-and-spoke architecture where the MCP server acts as the central coordinator. Incoming requests are analyzed by a router agent that determines the optimal execution path. Domain agents communicate through the MCP server using a standardized message protocol, with Redis providing both caching and message queuing capabilities.
Key Features
Domain-Specific Agents
Specialized agents for research, coding, analysis, and communication tasks, each fine-tuned for optimal performance in their domain.
Custom MCP Server
A Model Context Protocol server that orchestrates agent communication, manages context windows, and handles tool invocations efficiently.
Redis Caching Layer
Intelligent caching system that stores frequently used tool results and context, dramatically reducing API calls and response times.
Dynamic Task Routing
Smart routing system that analyzes incoming requests and delegates to the most appropriate agent based on task complexity and domain.
Tech Stack
Backend
AI/ML
Database
DevOps
Challenges & Solutions
High latency from repeated tool invocations across multiple agents, causing slow response times for complex queries.
Implemented a multi-level Redis caching strategy that stores tool results with intelligent TTL based on data volatility, reducing redundant API calls by 60%.
Token usage exploding due to context being passed between multiple agents for multi-step tasks.
Designed a context compression system that summarizes relevant information before inter-agent communication, cutting token usage by 50% while maintaining accuracy.
Difficulty in maintaining consistent behavior across domain-specific agents.
Created a shared behavioral framework with standardized prompts and output formats, ensuring consistent user experience regardless of which agent handles the request.