AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
AgentCo-op is a retrieval-based synthesis framework that composes reusable skills, tools, and external agents into executable workflows via typed artifact handoffs, with bounded self-guided local repair on failure. In open-world genomics case studies, it coordinates independently developed agents and tool repositories without redesign. On six benchmarks, it achieves top results on four and reduces per-task costs consistently.
[2605.20425] AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
[Submitted on 19 May 2026]
Title:AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
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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 studies, AgentCo-op composes independently developed scientific agents and external tool repositories into auditable workflows without redesigning them or running global topology search. It coordinates specialized agents for spatial transcriptomics and gene-set interpretation to enable collaborative discovery from spatial transcriptomics data, and builds a parallel workflow for cross-modality marker analysis on single-cell multiome data. AgentCo-op can also import a searched workflow as a structural prior and improve it by grounding nodes with retrieved components and applying local repair, showing that synthesis and search are complementary. On six coding, math, and question-answering benchmarks, AgentCo-op achieves the best result on four benchmarks and the best average score under a unified backbone setting, while consistently reducing per-task cost relative to multi-agent baselines. Together, these results suggest that retrieval-based synthesis can extend automated agentic workflow design beyond benchmark-optimized agent graphs to open-world workflows built from existing agents, tools, and typed artifacts.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.20425 [cs.AI]
(or arXiv:2605.20425v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.20425
arXiv-issued DOI via DataCite (pending registration)
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From: Shuaike Shen [view email] [v1] Tue, 19 May 2026 19:22:21 UTC (5,586 KB)
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