CACA Agent: Capability Collaboration based AI Agent

TL;DR

The paper introduces CACA Agent, an open-architecture AI agent framework that enhances extensibility and reduces dependence on a single LLM by integrating collaborative capabilities inspired by service computing.

Contribution

It proposes a novel open architecture for AI agents that improves scalability and flexibility through capability collaboration, moving beyond single LLM reliance.

Findings

Demonstrates improved extensibility in AI agents

Shows reduced dependence on a single LLM

Provides a practical demo of application scenario extension

Abstract

As AI Agents based on Large Language Models (LLMs) have shown potential in practical applications across various fields, how to quickly deploy an AI agent and how to conveniently expand the application scenario of AI agents has become a challenge. Previous studies mainly focused on implementing all the reasoning capabilities of AI agents within a single LLM, which often makes the model more complex and also reduces the extensibility of AI agent functionality. In this paper, we propose CACA Agent (Capability Collaboration based AI Agent), using an open architecture inspired by service computing. CACA Agent integrates a set of collaborative capabilities to implement AI Agents, not only reducing the dependence on a single LLM, but also enhancing the extensibility of both the planning abilities and the tools available to AI agents. Utilizing the proposed system, we present a demo to illustrate the operation and the application scenario extension of CACA Agent.