Why Do Long-Horizon Agents Break? Diagnosing Failure with HORIZON and Related Papers

The paper "The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break" introduces HORIZON as an initial cross-domain diagnostic benchmark for long-horizon agent tasks. Its starting point is straightforward: LLM agents often look strong on short- and mid-horizon tasks, but they frequently fail when a task requires long, interdependent sequences of actions, and those failures are still not well characterized. [S1] [S1]

HORIZON: the problem it is trying to name

HORIZON is presented as a response to a specific gap in agent evaluation. According to the paper, long-horizon failures in agentic systems remain poorly characterized, which makes principled diagnosis and comparison difficult across domains. The key point is not simply that agents fail on longer tasks, but that these tasks depend on extended action chains where earlier decisions constrain later ones. In that setting, a system can appear competent in isolated steps while still collapsing over the full trajectory. HORIZON is therefore framed as a cross-domain benchmark for diagnosing where and why those breakdowns happen, rather than as a complete solution to long-horizon planning itself. [S1]

Sources: [S1]

Core idea: diagnose failure in long, interdependent action sequences

The central idea behind HORIZON is to treat long-horizon performance as a diagnostic problem, not just a final-score problem. The source emphasizes that long-horizon tasks involve extended, interdependent action sequences. That matters because failure may come from many places: a weak early plan, a mistaken intermediate action, loss of context over time, or inability to recover after a small error. My interpretation is that HORIZON is useful because it shifts attention from "Did the agent finish?" to "At what stage did the task begin to unravel, and why?" This is especially important for nontrivial agent workflows, where a single local mistake can propagate through later steps. [S1]

Sources: [S1]

What is different from existing evaluation: stricter views of planning and finer-grained diagnosis

Related work helps explain why this diagnostic framing matters. The paper on long-horizon plan execution in large tool spaces argues that current agent settings face two bottlenecks: the lack of rigorous, plan-level evaluation frameworks and the computational difficulty of exploring large decision spaces created by many tools and long planning horizons. That suggests that simple end-task success rates are often too coarse for understanding plan execution quality. [S8]

A similar critique appears in work on diagnostic LLM evaluation beyond aggregate scores. That paper argues that single scores hide fine-grained ability variation, making targeted improvement harder. Although its main example is mathematics rather than agent planning, the broader lesson transfers well: if evaluation compresses many abilities into one number, it becomes difficult to identify the actual source of failure. In my reading, HORIZON fits this broader shift toward more structured diagnosis, where the goal is not only ranking systems but also exposing which capabilities break under long-horizon pressure. [S12][S1]

Sources: [S8], [S12], [S1]

Practical relevance: tool-heavy agents, multi-step execution, and hidden decision risks

These ideas are most relevant in settings where agents must operate over many steps and many tools. The large-tool-space planning paper highlights the difficulty of executing multi-step tasks when the agent must reason over massive tool libraries and a wide decision space. In practical terms, this points to software agents, API-driven workflows, and other environments where choosing the wrong tool or sequence can derail the whole plan. [S8]

Another useful connection comes from the paper on policy-invisible violations. It describes cases where an LLM agent takes actions that are syntactically valid, user-sanctioned, and semantically appropriate, yet still violate organizational policy because the facts needed for correct judgment are hidden from the agent at decision time. This expands the notion of long-horizon failure: the problem is not only whether the agent can complete a sequence, but whether it can do so safely and compliantly when relevant context is partially invisible. That makes diagnostic benchmarks and plan-level evaluation potentially useful not just for task completion, but also for identifying where hidden-state or missing-context risks enter the workflow. [S11]

Sources: [S8], [S11]

Limitations and open questions

The sources also make clear that this area is still early. HORIZON is described as an initial cross-domain diagnostic benchmark, which implies a starting framework rather than a final answer. It helps characterize long-horizon breakdowns, but the source does not claim that it solves them across all domains. [S1]

There are also limits to what diagnosis alone can reveal. The policy-invisible violations paper shows that some failures depend on information absent from the agent's visible context, including entity attributes, contextual state, or session history. In such cases, even a well-designed benchmark may identify the failure pattern without fully removing the underlying visibility problem. [S11]

Finally, the fine-grained evaluation paper reminds us that better diagnosis creates its own design challenge: deciding which abilities matter, how to define them, and how to measure them consistently. A richer diagnostic framework is more informative than a single score, but it is also harder to standardize. So the current direction looks promising, but still incomplete: long-horizon agent evaluation is moving from broad outcome scoring toward more detailed analysis, and many domain-specific questions remain open. [S12][S1]

Sources: [S1], [S11], [S12]

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One-line takeaway: HORIZON frames long-horizon agent failure as a diagnostic problem across domains, and related papers suggest that plan-level evaluation and fine-grained analysis are necessary because single scores often hide where long, tool-rich, interdependent workflows actually break. [S1][S8][S12] [S1] [S8] [S12]

Short summary: HORIZON proposes an initial cross-domain benchmark for diagnosing where long-horizon LLM agents break. Related papers show why single scores are often too coarse, especially in tool-heavy planning and policy-sensitive workflows.

Sources and references: - [S1] cs.AI updates on arXiv.org - The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break - URL: https://arxiv.org/abs/2604.11978 - [S8] cs.AI updates on arXiv.org - Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching - URL: https://arxiv.org/abs/2604.12126 - [S11] cs.AI updates on arXiv.org - Policy-Invisible Violations in LLM-Based Agents - URL: https://arxiv.org/abs/2604.12177 - [S12] cs.AI updates on arXiv.org - Beyond Scores: Diagnostic LLM Evaluation via Fine-Grained Abilities - URL: https://arxiv.org/abs/2604.12191

Internal link ideas: - How to evaluate LLM agents beyond task success rate - What makes tool-using agents fail in large API environments - Why hidden context causes policy violations in AI agents

LLM agents #HORIZON #long-horizon tasks #agent evaluation #tool use #planning

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