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The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break

Xinyu Jessica Wang, Haoyue Bai, Yiyou Sun, Haorui Wang, Shuibai Zhang, Wenjie Hu, Mya Schroder, Bilge Mutlu, Dawn Song, Robert D Nowak

arXiv:2604.11978v1cs.AI

TL;DR

Long-horizon agent failures remain poorly characterized because extended, interdependent action sequences expose breakdowns that current evaluations do not systematically compare or diagnose. HORIZON introduces a cross-domain benchmark with controlled horizon construction and trajectory-grounded failure attribution, finding that planning and memory failures become dominant as horizons grow. The paper uses these findings to motivate design improvements beyond base-model scaling, while acknowledging scope boundaries in its judge-based analysis and memory-related failure coverage.

  • Problem

    Long-horizon agent failures are poorly characterized because existing evaluations provide limited, inconsistent evidence about where and why breakdowns arise across domains.

  • Method

    HORIZON combines agent-independent horizon metrics, controlled task extension, cross-domain evaluation, and trajectory-grounded LLM-as-a-Judge failure attribution.

  • Results

    Across 3100+ trajectories, long-horizon breakdown shows a structural shift in which planning-related errors and catastrophic forgetting become dominant as horizon increases.

  • Takeaways & Limitations

    Reliable long-horizon agents require method-level improvements in planning, memory, and execution-time control rather than base-model scaling alone.

  • Takeaways & Limitations

    The study uses large language models as automated judges for failure attribution and identifies memory limitation failures as a constrained failure category when effective memory capacity is exceeded.

Abstract

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Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, interdependent action sequences. Despite rapid progress in agentic systems, these long-horizon failures remain poorly characterized, hindering principled diagnosis and comparison across domains. To address this gap, we introduce HORIZON, an initial cross-domain diagnostic benchmark for systematically constructing tasks and analyzing long-horizon failure behaviors in LLM-based agents. Using HORIZON, we evaluate state-of-the-art (SOTA) agents from multiple model families (GPT-5 variants and Claude models), collecting 3100+ trajectories across four representative agentic domains to study horizon-dependent degradation patterns. We further propose a trajectory-grounded LLM-as-a-Judge pipeline for scalable and reproducible failure attribution, and validate it with human annotation on trajectories, achieving strong agreement (inter-annotator κ=0.61; human-judge κ=0.84). Our findings offer an initial methodological step toward systematic, cross-domain analysis of long-horizon agent failures and offer practical guidance for building more reliable long-horizon agents. We release our project website at \href{https://xwang2775.github.io/horizon-leaderboard/}{HORIZON Leaderboard} and welcome contributions from the community.

1 Introduction

Long-horizon agent failures are difficult to compare and diagnose because existing evaluations use inconsistent, domain-specific notions of horizon and mostly report aggregate success. HORIZON addresses this gap with cross-domain task construction, controlled evaluation, and trajectory-grounded failure attribution.

  • Long-horizon tasks require agents to maintain goals, constraints, and intermediate states across dozens or hundreds of effective actions.
  • Existing research uses domain-specific benchmarks and inconsistent horizon definitions, limiting cross-domain comparison and insight into failure causes.
  • HORIZON constructs task families with systematically increasing step requirements and analyzes where and why agents fail as horizons grow.
  • HORIZON evaluates GPT-5 variants and Claude-4 across 3100+ trajectories in four representative domains.
  • The trajectory-grounded LLM-as-a-Judge pipeline achieves κ=0.61 inter-annotator agreement and κ=0.84 human–judge agreement on a pilot set.
  • Long-horizon failure involves a structural shift in composition: planning-related and memory-related failures become dominant as horizon increases.

2 Related Work

Related work spans interactive benchmarks, agent training and planning, memory systems, and failure analysis. HORIZON differs by targeting horizon-conditioned, trajectory-level failure diagnosis across domains.

  • Interactive benchmarks cover web navigation, software engineering, app assistants, databases, and embodied instruction following.
  • Prior methods improve agents through reinforcement learning, hierarchical planning, tool use, reflection, deliberation, and reliability studies.
  • Long contexts, retrieval, external memory, trajectory compression, and hybrid stacks address scaling but do not by themselves ensure long-range reasoning.
  • Existing failure studies are often domain-specific or coarse, while multi-agent taxonomies target coordination rather than single-agent horizon-conditioned failures.
  • HORIZON builds a cross-domain FMEA-grounded taxonomy and validates trajectory-level labels for structured failure analysis.

3 HORIZON

HORIZON separates intrinsic task structure from agent inefficiency through agent-independent horizon metrics and controlled task extension. It complements these measurements with a seven-category, trajectory-grounded failure taxonomy validated by human and LLM annotation.

  • 3.1 Defining Task Horizons: HORIZON uses theoretical metrics for agent-independent task structure and technical implementations for systematic task extension.
  • 3.1 Defining Task Horizons: Intrinsic Horizon H∗ measures the minimum effective actions required by an optimal policy, independent of an individual agent’s extra attempts.
  • 3.1 Defining Task Horizons: Compositional Depth s measures nested sub-goals or conditional branches, capturing planning complexity beyond linear execution.
  • 3.1 Defining Task Horizons: Controlled horizon extension increases H∗ through depth and breadth constructions while accounting for domain-dependent task structure.
  • 3.2 Failure Attribution: 7-Category Taxonomy: HORIZON derives a seven-category taxonomy grounded in FMEA, agent workflow, and literature across web, OS, embodied, and database settings.
  • 3.2 Failure Attribution: 7-Category Taxonomy: Taxonomy categories are orthogonal rather than mutually exclusive, allowing one failed trajectory to receive multiple diagnoses.
  • 3.2 Failure Attribution: 7-Category Taxonomy: Two expert annotators achieved κ=0.61 agreement on 40 failure trajectories, supporting consistent interpretation of the taxonomy.

4 Empirical Performance of SOTA Models

Across domains, agent performance declines nonlinearly with controlled horizon extension and then enters sharp, domain-specific collapse regions. Failed trajectories can also be consistently attributed using the seven-category taxonomy.

  • Nested task sets control adjacent horizon comparisons by retaining earlier tasks and adding longer-execution tasks.
  • Performance degrades nonlinearly as compositional depth s increases, with sharp drops beyond early extension levels across domains.
  • The same extension level produces different difficulty: Web collapses early, OS and Database remain moderately robust longer, and Embodied degrades steeply.
  • Model performance gaps narrow substantially after Web, OS, and Database tasks enter the breaking region.
  • LLM judges and human annotators consistently apply the seven-category taxonomy across all four domains.

5 Actionable Steps Toward Responsible Long-Horizon Agent Development

The paper translates its diagnostic framework into cross-domain horizon measurement and scalable failure attribution, while emphasizing planning, memory, and execution-time controls for more reliable agents.

  • Implications for Future Agentic AI Systems: A universal breaking point is unattainable, so cross-domain diagnosis should focus on structured failure categories and practical research tools.The proposed steps translate the diagnostic framework into tools for researchers and practitioners.
  • Unified Horizon Task Construction and Measurement Across Domains: Unified horizon-aware task construction and standardized horizon metrics can support principled cross-domain comparison.HORIZON addresses heterogeneous task designs and horizon definitions that obscure where long-horizon failures arise.
  • Scalable Failure Attribution via LLM-as-a-Judge: LLM-based judges can provide scalable, consistent, and actionable failure attribution for thousands of trajectories.The approach is intended to complement, rather than fully replace, human evaluation.
  • Implications for Future Agentic AI Systems: Planning-related failures, memory limitation, and catastrophic forgetting account for a substantial portion of failed trajectories across domains.Planning errors can arise early and propagate through downstream actions, converting recoverable local mistakes into broader failures.

6 Conclusion

HORIZON provides an initial cross-domain framework for constructing and diagnosing long-horizon tasks. Across 3100+ trajectories, it finds that failures change composition with horizon, highlighting the need for system-level improvements beyond base-model scaling.

  • Conclusion: HORIZON is an initial cross-domain benchmark for systematic long-horizon task construction and failure-trajectory analysis.The study spans 3100+ trajectories and multiple model families.
  • Conclusion: Long-horizon breakdown is a structural shift in failure composition as horizon grows, not merely a drop in task success.This conclusion reframes evaluation around which failure types emerge or become dominant.
  • Conclusion: Planning-related errors and catastrophic forgetting emerge as dominant bottlenecks across domains.Early subplanning deviations propagate through later actions, while increasing context load exacerbates memory and constraint-retention failures.
  • Conclusion: The paper proposes trajectory-grounded LLM-as-a-Judge diagnosis validated against human annotations.It presents this pipeline as support for scalable and reproducible analysis.

7 Real-World Mapping: OpenClaw

The OpenClaw mapping illustrates how the proposed taxonomy can describe concrete agent incidents, including environmental disruption, instruction reframing, runaway planning, forgotten constraints, identity assumptions, history effects, and memory accumulation.

  • Real-World Mapping: OpenClaw: The OpenClaw incidents are presented as preliminary real-world grounding for HORIZON’s controlled-evaluation failure categories.The mapping examines whether practical failures align with the proposed diagnostic taxonomy.
  • Environment Disturbance: A cloud-sync rename caused stale-state execution and propagated incorrect file placements during a multi-step reorganization task.The agent treated file-not-found signals as transient instead of detecting environmental disruption and halting.
  • Instruction Error: Instruction reframing exposed inconsistent treatment of semantically equivalent requests, enabling sensitive-data disclosure.The agent refused “share” but complied with “forward” for the same messages.
  • Planning Error: Missing termination checks or loop detection produced runaway behavior when two agents relayed messages indefinitely.The incident is classified as a planning error.
  • Catastrophic Forgetting: A long-running agent forgot an explicit external-domain constraint despite its continued presence in context.The policy override occurred after hundreds of routine internal turns.
  • Other Failure Categories: The remaining incidents illustrate false assumptions, history error accumulation, and memory limitation as distinct failure categories.They involve unverified identity, attacker-favorable interaction history, and cumulative storage actions that eventually caused denial of service.

A.4 Failure analysis and diagnostics

Prior failure studies provide useful domain-specific or coarse categories, but they generally do not analyze how failure patterns shift as task horizon increases.

  • Failure Analysis and Diagnostics: Existing failure studies largely focus on domain-specific issues such as code-generation errors, web interaction failures, and embodied stochasticity.Broader benchmarks can provide coarse labels, including hallucination and planning errors.
  • Failure Analysis and Diagnostics: AgentBench reports high-level failure tags but does not study how failure patterns change with increasing task horizon.This leaves a gap between broad benchmarking and horizon-dependent diagnosis.

B.1 Environment Error

Environment and instruction-related failures arise from mismatches between task requirements, agent beliefs, and changing external states, while long-horizon execution can amplify these errors through forgetting and accumulated history mistakes.

  • Environment Error: Environment failures occur when external conditions change, degrade, or behave stochastically in ways the agent’s plan does not model.Examples include network instability, delayed rendering, pop-ups, schema drift, protocol mismatches, and stochastic physical dynamics.
  • Environment Error: Agents may fail to detect environmental changes, causing their internal belief states to diverge from actual UI, OS, or physical states.This can produce false transitions or missed transitions during interaction.
  • Instruction Error: Instruction failures arise when goals are ambiguous, unreachable, underspecified, or only partially understood, causing agents to violate constraints or propagate incorrect interpretations.Long-horizon decomposition increases opportunities for ambiguity and partial understanding to affect later subtasks.
  • Memory Error: Catastrophic forgetting is failure to retain and consistently apply earlier knowledge, constraints, decisions, or prerequisite skills during continued operation.It can result from limited context, inadequate retrieval, or interference from newly introduced information.
  • History Error: History error accumulation occurs when early mistakes or incorrect intermediate artifacts persist in working state and progressively distort downstream reasoning and actions.Later steps may remain internally consistent while being fundamentally wrong because they reuse flawed tables, schemas, edits, hypotheses, or plans.

C Appendix: Literature-to-Taxonomy Mapping

The appendix maps representative literature to HORIZON’s taxonomy and describes the benchmark’s controlled, cross-domain construction and frontier-model evaluation setup.

  • Literature-to-Taxonomy Mapping: The literature review records domains, models, venues, and reported failure mechanisms, then aligns recurring mechanisms to the seven-category taxonomy through iterative coding.Table 2 presents three representative prior works per category as illustrative rather than exhaustive anchors.
  • Framework Scope: HORIZON treats breaking points as model- and domain-conditional transition regions because horizon capacity varies with environment dynamics, observability, and action semantics.A universal threshold is therefore unattainable within the proposed framing.
  • Framework Scope: The framework enables controlled horizon extension and comparable analysis along horizontal horizon growth and vertical failure attribution dimensions.The authors present this as an initial step toward principled long-horizon research.
  • Data and Evaluation: Baseline tasks are verified as solvable before horizon extension so degradation can be attributed to increased horizon rather than ambiguity or agent misconfiguration.The benchmark uses this procedure across domains.
  • Data and Evaluation: The evaluation spans web navigation, operating systems, databases, and embodied tasks, using domain-specific environments and controlled task construction.The database pipeline includes selection, decomposition, refinement, execution, error detection, and iterative query revision; embodied plans use predefined JSON actions executed in simulation.
  • Data and Evaluation: Two representative frontier model families, GPT-5-mini and Claude-4-Sonnet, are evaluated across all four domains.This provides a cross-family feasibility test of the benchmark.

D.3 Evaluation

HORIZON evaluates agent performance as horizon extension increases and analyzes failed trajectories with a seven-category taxonomy. The examples show failures involving navigation repetition, task drift, misinterpretation, premature stopping, and forgetting previously obtained information.

  • HORIZON reports success rates at matched horizon extension levels and analyzes failures using human annotation and calibrated LLM-based judges.
  • Embodied success declines sharply with increasing horizon for both GPT-5 variants and Claude-4.GPT-5 variants decrease from 0.926 ± 0.064 to 0.074 ± 0.064, while Claude-4 decreases from 0.963 ± 0.064 to 0.037.
  • Agents repeatedly issue the same failed navigation action after recognizing a Chrome error page.The trajectory repeats goto localhost:4399 despite noting that the URL is inaccessible.
  • A multi-part shopping task fails when the agent abandons the required answer and continues unrelated navigation or shopping actions.The agent identifies a cancelled-order total of $365.42 but continues navigating and adding detergent instead of completing the requested response.
  • Other trajectories show premature stopping, incorrect interpretation of records, incoherent switching between workflows, and task drift into unrelated file-counting commands.Examples include stopping with a narrative instead of “Canceled,” treating 126 records as the review total, switching back to Reports, and counting files under /etc.
  • The agent can correctly observe the required order total yet later behave as if the information were unavailable, repeatedly revisiting the same page.The trajectory records order 000000170 with total $365.42 before the agent navigates away and retries retrieval.

E.4 Database

The database examples evaluate failures on multi-part analytical questions requiring schema interpretation, joins, decomposition, and coordinated aggregation. The trajectories include incorrect SQL, incomplete query construction, and failure to retain or apply relevant information across steps.

  • Database tasks require interpreting schemas and answering multi-part questions involving gender, salary, loan status, districts, and account activity.
  • Another database task fails because the agent filters only the globally minimum-salary district before selecting the oldest female client.The gold SQL instead orders by birth date and salary and limits the result to one account.
  • An agent incorrectly joins account data on district identifiers and computes birth-date statistics instead of the requested district-level statistics.The gold query uses district-level CTEs and joins to account activity, contrasting with the agent’s incorrect SQL.
  • A high-school statistics request is reduced to a single high-poverty count rather than the required enrollment, charter, SAT, school-size, and poverty metrics.The gold query uses multiple CTEs, while the notes identify failure to decompose the multi-part request.
  • The failure taxonomy distinguishes environment, instruction, forgetting, assumptions, planning, history accumulation, and memory limitations using proximal turning-point causes.The categories are applied as diagnostic dimensions to long-horizon mini-trajectories rather than as mutually exclusive broad labels.

F Appendix: LLM-as-a-Judge Result

Across 3,132 trajectories, success varied substantially by domain and model, while failure profiles differed between GPT and Claude. Planning errors dominated overall, but Claude showed more environment and instruction failures, whereas GPT showed more memory-related failures.

  • Overall results: 3,132 trajectories yielded an overall task success rate of 36.3%, with domain success rates ranging from 24.1% in Web to 42.2% in Embodied.OS achieved 40.3% and Database 36.9%.
  • Model profiles: GPT failures were dominated by Planning Errors at 64.9% and Memory Limitation at 18.3%.The GPT aggregate comprised 1,145 failed traces and a 33.4% success rate.
  • Model profiles: Claude failures contained more Environment failures at 32.5% and Instruction failures at 16.5%, while Planning Errors remained most common at 46.5%.Claude had 850 failed traces and a 39.9% success rate.
  • Domain profiles: Embodied and Database failures were dominated by Planning Error at 94.9% and 79.3%, respectively.Their success rates were 42.2% and 36.9%.
  • Domain profiles: OS had the most diverse failure profile, with Planning, Instruction, Environment, and Memory Limitation all contributing substantially.Their shares were 36.7%, 25.9%, 17.3%, and 15.1%, respectively.
  • Domain-model interactions: Across domains, GPT and Claude showed distinct secondary mechanisms: GPT had more Memory Limitation, while Claude had more False Assumption in Web and stronger Environment effects in OS.Database and Embodied included only GPT trajectories in the dataset.

G Appendix: More Discussion

The discussion presents HORIZON as evidence that long-horizon failures involve domain-dependent transition regions and changing failure composition, not merely lower success rates. It argues for diagnosis-centered, horizon-aware evaluation and targeted interventions rather than scaling alone.

  • Empirical support for HORIZON: HORIZON observed performance collapse beyond early horizon-extension levels across all four domains, with failures fitting its seven-category taxonomy.The authors interpret this as support for ordering tasks by long-horizon difficulty and for the taxonomy’s cross-domain applicability.
  • Definition and comparability: Prior benchmarks use incompatible horizon proxies, making long-horizon tasks difficult to define and compare across domains.Examples include interaction length, tool calls, reasoning steps, and episode duration.
  • Failure interpretation: Long-horizon failures are framed as generalization under distribution shift involving compounding changes in states, consequences, and goal dependencies.The discussion distinguishes this from simply extending short-horizon planning errors.
  • Scaling and intervention: Performance gaps often collapse in the long-horizon failure regime, suggesting diminishing returns from model capacity alone.The discussion associates different failure types with different interventions, including monitoring, memory improvements, and sub-goal decomposition.
  • Breaking points: Breaking points are better treated as transition regions spanning narrow horizon ranges than as precise universal thresholds.The discussion recommends identifying regions where failure dynamics change qualitatively.
  • Benchmark design and evaluation: Single-point accuracy obscures how failures emerge, whereas horizon-aware evaluation reports performance curves, transitions, and attributed failure types.HORIZON separates horizon measurement from failure attribution without assuming a universal breaking point.
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