Meta-Agent: Automated Multi-Agent System Construction from Natural Language
Researchers have introduced a novel framework named Meta-Agent, which autonomously creates and implements specialized multi-agent AI systems based on natural-language task descriptions. This development, detailed in a paper on arXiv, seeks to overcome the limitations of current multi-agent frameworks as workflows expand. During its construction, a task planner breaks down a problem into a directed acyclic graph comprising agent specifications, each defined by clear input/output contracts and verification standards. An external evidence grounding module supports each specification, while a code generation component creates system prompts and tool setups. A verification phase ensures the accuracy of generated artifacts and initiates targeted regeneration upon detecting failures. This method aims to enhance reliability by minimizing error propagation through intermediate agent interactions, a prevalent issue in existing systems. The paper can be found at arXiv:2605.25233.
Key facts
- Meta-Agent is a two-phase framework for constructing multi-agent systems from natural-language descriptions.
- It uses a task planner to decompose problems into directed acyclic graphs of agent specifications.
- Each agent specification includes input/output contracts and verification criteria.
- A web search module grounds specifications with external evidence.
- A code generation module produces system prompts and tool configurations.
- Construction-time verification validates artifacts and triggers regeneration on failure.
- The framework aims to reduce brittleness and error propagation in multi-agent workflows.
- The paper is published on arXiv with ID 2605.25233.
Entities
Institutions
- arXiv