Meta-Agent: From Task Descriptions to Verified Multi-Agent Systems
The paper introduces Meta-Agent, a framework designed to improve the reliability of multi-agent systems. It automates the construction and execution of these systems from natural-language task descriptions, addressing issues of error propagation and verification. The framework has shown consistent improvements in task success rates and workflow stability through integrated planning and verification mechanisms.
- ▪Meta-Agent constructs multi-agent systems from natural-language task descriptions.
- ▪The framework includes a two-phase process: construction and execution.
- ▪Experiments demonstrate improvements in task success rates and error recovery.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25233 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.25233 (cs) [Submitted on 24 May 2026] Title:Meta-Agent: From Task Descriptions to Verified Multi-Agent Systems Authors:Andy Xu, Yu-Wing Tai View a PDF of the paper titled Meta-Agent: From Task Descriptions to Verified Multi-Agent Systems, by Andy Xu and Yu-Wing Tai View PDF HTML (experimental) Abstract:AI agents are increasingly used to solve complex, multi-step tasks, but existing multi-agent frameworks remain brittle as workflows grow in scale and depth. Small errors at intermediate stages can propagate through agent interactions, while insufficient grounding and weak verification mechanisms further limit reliability.
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