PROTEA: Offline Evaluation and Iterative Refinement for Multi-Agent LLM Workflows
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Computer Science > Computation and Language
arXiv:2605.18032 (cs)
[Submitted on 18 May 2026]
Title:PROTEA: Offline Evaluation and Iterative Refinement for Multi-Agent LLM Workflows
View a PDF of the paper titled PROTEA: Offline Evaluation and Iterative Refinement for Multi-Agent LLM Workflows, by Kazuki Kawamura and 2 other authors
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Abstract:Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform single-prompt baselines, but they remain difficult to debug and refine. Failures can originate from subtle errors in intermediate outputs that propagate to downstream nodes, requiring developers to inspect long traces and infer which agent to modify. We present PROTEA, a unified interface for offline, test-driven improvement of multi-agent workflows. PROTEA executes a workflow, scores intermediate node outputs with configurable rubrics, and overlays per-node states and rationales on the workflow graph to localize likely bottlenecks. To support complex systems where final-answer references are the primary supervision, PROTEA performs backward node evaluation: it generates candidate node-level expectations from final-answer references and graph context, then compares them with observed node outputs. For selected nodes, PROTEA presents targeted prompt revisions as editable before/after comparisons, then automatically reruns and re-evaluates the workflow to show output changes and score trajectories within the same interface. In two production-adjacent workflows, PROTEA improved document-inspection accuracy from 64.3% to 83.9% and recommendation Hit@5 from 0.30 to 0.38. In a formative study with six experienced LLM developers, participants valued graph-level localization, per-node rationales, and editable before/after prompt revisions.
| Comments: | 9 pages, 3 figures, 1 table. To appear in Proceedings of ACL 2026 System Demonstrations |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE) |
| ACM classes: | I.2.7; H.5.2; D.2.5 |
| Cite as: | arXiv:2605.18032 [cs.CL] |
| (or arXiv:2605.18032v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18032
arXiv-issued DOI via DataCite (pending registration)
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