Core Concepts

Core Concepts

This page explains the fundamental ideas behind this project.

1. Multi-Agent Systems (MAS)

A Multi-Agent System is a framework where multiple autonomous AI agents interact to solve problems that are beyond the capabilities of a single agent. In an LLM context, each agent can be specialized for a different function (e.g., planning, question-answering, validation, aggregation). 2. Homogeneous vs. Heterogeneous MAS

Homogeneous MAS: All agents use the same underlying LLM (e.g., all use GPT-4). This is simple but limited by that single model's biases and performance profile.

Heterogeneous MAS: Agents can use different LLMs (e.g., a coding agent uses Claude, a legal agent uses GPT-4). This allows for specialization but introduces the challenge of selecting the right model for the right job. Our project solves this selection problem.

3. The Dynamic Selection Problem

The core research question we address: Given a task and a pool of available LLMs, how do we automatically and dynamically select the best LLM to maximize performance (e.g., accuracy, speed, cost-efficiency)? 4. Model Context Protocol (MCP)

MCP is an open standard that allows LLMs to connect securely to external data sources and tools. In our architecture:

It acts as a standardized "database" for LLM performance metrics (the Score Matrix).

The Orchestrator Agent queries the MCP server via tool calls to get the data it needs to make its selection decision.

It decouples the knowledge of LLM performance from the agent logic, making the system more modular.

5. Agentic AI Orchestrator

This is the "brain" of the system. It's an LLM-powered agent itself, whose sole purpose is to reason about a task and select the best LLM for it. It uses the MCP server as its knowledge source and can incorporate complex, weighted priorities (e.g., "for this financial task, prioritize accuracy over latency").