Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents

By synthesizing findings from 27 benchmark, taxonomy, and audit papers spanning 19 benchmarks (2023-2026), this paper builds a unified six-cluster taxonomy showing that LLM agent failures in tool use, planning, long-horizon reasoning, coordination, safety, and evaluation are systemic and recurring rather than benchmark-specific anomalies.

Problem Statement

LLM agent research is fragmented across siloed benchmark and audit papers that each report their own error taxonomies, making it hard to see that the same underlying failure modes recur across tool use, planning, multi-agent, and safety literatures. Leaderboard-style reporting obscures these recurring weaknesses, giving a misleading impression of steady progress toward reliable autonomous agents and hampering practitioners' ability to anticipate failure modes before deployment.

Key Novelty

First cross-cutting synthesis unifying evidence from six previously separate research threads (tool use, planning, long-horizon reasoning, multi-agent coordination, safety/security, and measurement validity) into a single taxonomy
An iterative, bottom-up taxonomy construction method that maps independently reported error categories from 27 papers onto stages of the agent reasoning-to-action pipeline
Identification of cross-benchmark meta-patterns invisible within any single evaluation, such as nonlinear failure compounding with task length and the disconnect between sub-task and end-to-end success

Evaluation Highlights

Literature coverage: 27 benchmark/taxonomy/audit papers (2023-2026) analyzed across 19 distinct agent benchmarks
Qualitative cross-cutting findings: failures compound nonlinearly with task length, strong sub-task performance does not reliably predict end-to-end success, and added scaffolding does not consistently improve reliability, while genuine gains are documented in single-turn tool use, short-horizon web navigation, and narrow coding tasks

Signal Assessment

4/10 This is a valuable, well-scoped synthesis that organizes a fragmented field into a unified taxonomy, but it does not introduce a new model, benchmark, or empirical result that advances agent capability itself, placing it at the higher end of 'solid contribution' rather than a technical breakthrough.

Methodology

Systematically collected 27 benchmark, taxonomy, and audit papers (2023-2026) covering 19 distinct LLM agent benchmarks
Extracted independently reported failure/error categories from each source without assuming a predefined taxonomy
Iteratively grouped these categories into recurring themes via thematic/qualitative coding
Mapped resulting themes onto distinct stages of the agent reasoning-to-action pipeline to produce six failure clusters
Cross-analyzed clusters to surface meta-patterns (e.g., nonlinear degradation, scaffolding effects) that are not visible within any single benchmark paper

System Components

Tool invocation and parameter-level errors

Failures in correctly selecting, formatting, or parameterizing API/tool calls during agent execution

Planning and constraint-satisfaction failures

Breakdown in multi-step plan generation, including violated task constraints and invalid action sequencing

Long-horizon degradation from context accumulation

Performance decay over extended interactions as context grows, causing drift, forgetting, or error accumulation

Multi-agent coordination failures

Miscommunication, redundant or conflicting actions, and role/task misallocation among cooperating agents

Safety and security failures

Vulnerabilities to adversarial prompts, jailbreaks, and unsafe behavior under underspecified or adversarial task conditions

Measurement validity problems

Issues with benchmark design, metric interpretation, and evaluation methodology that inflate or obscure true agent capability

Results

Failure Cluster	Representative Manifestation	Where Progress vs. Persistent Gaps Observed
Tool invocation errors	Malformed or mis-parameterized tool/API calls	Strong progress in single-turn tool use
Planning & constraint failures	Invalid multi-step plans, violated task constraints	Persistent gaps in complex multi-step planning
Long-horizon degradation	Accumulating context causes drift over extended sessions	Failures scale nonlinearly with task length
Multi-agent coordination	Conflicting or redundant actions among cooperating agents	Persistent, underexplored relative to single-agent work
Safety/security failures	Susceptibility to adversarial or underspecified prompts	Persistent gaps despite added safety scaffolding
Measurement validity	Benchmark leakage, inconsistent or gameable metrics	Undermines confidence in reported leaderboard gains

Key Takeaways

Leaderboard improvements should not be read as evidence of production readiness; the same six failure modes recur across nearly all evaluated agent systems
Agent reliability degrades nonlinearly with task horizon length, so short-task benchmark success is a poor proxy for long-horizon deployment reliability
Adding scaffolding (memory, extra prompting layers, multi-agent orchestration) is not a reliable fix and can introduce new coordination failure modes
Evaluate agents end-to-end rather than on isolated sub-tasks, since strong sub-task metrics (e.g., tool-call accuracy) do not reliably predict full-task success
The most mature, deployable use cases today remain single-turn tool use, short-horizon web navigation, and narrowly scoped coding tasks, while long-horizon and multi-agent settings warrant caution

Abstract

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.