CACA Agent: Capability Collaboration based AI Agent
This addresses the problem of complex and less extensible AI agents for developers, though it appears incremental as it builds on existing service computing ideas.
The paper tackles the challenge of deploying and extending AI agents based on LLMs by proposing CACA Agent, which uses an open architecture with collaborative capabilities to reduce reliance on a single LLM and enhance extensibility, as demonstrated in a demo.
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.