arXiv:2605.20425v1 Announce Type: new Abstract: Designing multi-agent workflows is especially difficult in open-ended scientific settings where tasks lack curated training sets, reliable scalar evaluation metrics, and standardized interfaces between existing tools and agents. We propose AgentCo-op, a retrieval-based synthesis framework that composes reusable skills, tools, and external agents into executable workflows through typed artifact handoffs, then applies bounded self-guided local repair to implicated components when execution evidence indicates failure. In two open-world genomics case
The proliferation of advanced AI models and the increasing complexity of scientific and enterprise tasks are driving the need for more sophisticated multi-agent system design frameworks.
This development addresses a critical challenge in creating autonomous, interoperable AI systems, accelerating the automation of complex workflows in domains like scientific research and enterprise operations.
The ability to synthesize, repair, and deploy multi-agent workflows without extensive human supervision or pre-curated data sets will significantly expand the scope and reliability of AI automation.
- · AI platform developers
- · Bioinformatics and genomics researchers
- · Enterprises adopting AI automation
- · AI agent developers
- · Tasks requiring manual workflow orchestration
- · Legacy automation software vendors
- · Companies slow to adopt agentic systems
AgentCo-op facilitates the creation of robust, self-correcting multi-agent systems for scientific and enterprise applications.
This framework will accelerate discovery in complex fields by automating iterative experimentation and data analysis, potentially leading to new scientific breakthroughs.
The widespread adoption of such agentic systems could reshape professional work by making entire white-collar workflows autonomous, leading to significant productivity gains and job market shifts.
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Read at arXiv cs.AI