arXiv AI
Why Agentic Workflows Fail: A Taxonomy Derived from Literature and Empirical Data
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arXiv AI
Jun 30
Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem
arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.
arXiv AI
Aug 3
Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures
arXiv:2607. 28802v1 Announce Type: new Abstract: Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system.
arXiv AI
Jul 2
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
arXiv:2607. 00828v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows.
Hugging Face Trending Papers
Jul 1
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workflow generation and execution, the semantic information required to operationalize analytical concepts often lies beyond what is explicitly represented in database schemas and data values.
Hugging Face Trending Papers
Jul 7
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts.
arXiv AI
Sep 24
Silent Failures in Agent-Tool Interaction: An Audit of ToolUniverse
The paper audits silent failures in agent-to-tool interactions within the ToolUniverse environment, focusing on 15 scientific tools. It identifies 91 failures—primarily missing data or inconsistent search/filtering—occurring mainly in the API and wrapper layers, and shows how these silent failures can propagate downstream into seemingly valid outputs. The authors propose contextual reliability and recommend testing, disclosure, monitoring, and measurement strategies for the agent-tool pipeline.