Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
06:00 · July 8, 2026 · arXiv cs.AI RSS
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.
Summary
Large language model agents are now routinely assessed on their capacity to invoke external tools, decompose multi-step objectives, collaborate with peer agents, and sustain performance across extended sequences of actions. Despite visible gains on public leaderboards, a recurring set of failure modes continues to appear across independent evaluations. This paper consolidates findings from 27 benchmark, taxonomy, and audit studies published between 2023 and 2026, covering 19 separate benchmarks, into a single cross-cutting taxonomy of limitations.
The resulting taxonomy organises observed errors into six clusters that map onto successive stages of the reasoning-to-action pipeline. These clusters comprise tool invocation and parameter-level mistakes, failures to satisfy planning constraints, progressive degradation over long horizons caused by context accumulation, breakdowns in multi-agent coordination, safety and security lapses under adversarial or ambiguously specified conditions, and problems of measurement validity that complicate interpretation of reported results. The clusters were assembled iteratively by aligning error categories reported in the source literature with distinct phases of agent operation.
Across the examined studies, errors accumulate in a nonlinear fashion as task length increases, and competence on isolated sub-tasks does not reliably predict success on complete end-to-end workflows. Additional scaffolding mechanisms likewise fail to deliver consistent reliability gains. At the same time, the literature records measurable progress on narrowly defined problems such as single-turn tool calls, short-horizon web navigation, and tightly scoped coding assignments.
Why it matters
This synthesis is highly relevant for Dutch AI researchers and developers building autonomous agents, as it provides a structured understanding of current LLM limitations. Its focus on safety, security, and measurement validity aligns strongly with the Netherlands' and EU's regulatory emphasis on robust, transparent, and ethical AI systems.
