Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
Quick Answer
This paper synthesizes findings from 27 benchmarks to identify six key failure modes in large language model (LLM) agents, including tool invocation errors and planning failures.
Quick Take
This paper synthesizes findings from 27 benchmarks to identify six key failure modes in large language model (LLM) agents, including tool invocation errors and planning failures. It reveals that performance on sub-tasks does not guarantee overall success and highlights the non-linear compounding of failures with task complexity, despite progress in specific areas like single-turn .
Key Points
- Identifies six failure clusters in LLM agents: tool errors, planning failures, and more.
- Performance on sub-tasks does not reliably predict end-to-end success.
- Failures compound nonlinearly with increased task length.
- Progress noted in single-turn tool use and short-horizon tasks.
- First unified taxonomy of LLM agent limitations across diverse benchmarks.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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. This paper synthesizes 27 benchmark, taxonomy, and audit papers (2023-2026), spanning 19 distinct benchmarks, into a cross-cutting taxonomy of agent limitations. To our knowledge, this is the first synthesis that integrates evidence across tool use, planning, long-horizon reasoning, multi-agent coordination, safety, and measurement validity into a single, unified taxonomy of LLM agent limitations. We identify six failure clusters: (1) tool invocation and parameter-level errors, (2) planning and constraint-satisfaction failures, (3) long-horizon degradation from context accumulation, (4) multi-agent coordination failures, (5) safety and security failures under adversarial or underspecified conditions, and (6) measurement validity problems. The taxonomy was derived iteratively by grouping independently reported error categories into themes corresponding to distinct stages of the agent reasoning-to-action pipeline. Across the literature, we find that failures compound nonlinearly with task length, that strong performance on individual sub-tasks does not reliably translate into end-to-end success, and that additional scaffolding does not consistently improve reliability. At the same time, substantial progress has been demonstrated in single-turn tool use, short-horizon web navigation, and narrowly scoped coding tasks.
| Comments: | 16 pages, 3 tables, 1 figure |
| Subjects: | Artificial Intelligence (cs.AI) |
| MSC classes: | cs archives only |
| Cite as: | arXiv:2607.05775 [cs.AI] |
| (or arXiv:2607.05775v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05775 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wael Albayaydh [view email]
[v1]
Tue, 7 Jul 2026 03:05:13 UTC (175 KB)
— Originally published at arxiv.org
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