AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows

• URL: http://arxiv.org/abs/2605.20425v1
• Date: Tue, 19 May 2026 19:22:21 GMT
• Title: AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
• Abstract summary: AgentCo-op is a retrieval framework that composes reusable skills, tools, and external agents into executable artifacts.<n>It applies self-guided local repair to implicated components when execution indicates failure.<n>It coordinates specialized agents for spatialomics and gene-set interpretation, and builds a parallel workflow for cross-modality marker analysis on single-cell multiome data.
• Score: 5.357463600626556
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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