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

This paper introduces HORIZON, a cross-domain diagnostic benchmark and evaluation framework that systematically analyzes long-horizon failure patterns in state-of-the-art LLM agents, proposes a validated LLM-as-a-Judge pipeline for failure attribution, and provides practical guidance for building more reliable agentic systems.

The Gist

The Long-Horizon Task Mirage: Why AI Agents Get Lost on Long Journeys

Imagine you ask a very smart, very eager robot assistant to do a simple task: "Buy me a wireless headphone under $200 with the highest rating."

If the robot just needs to click one button, it's great. But if the task requires a 20-step journey—searching, filtering, comparing, checking reviews, navigating pop-ups, and finally paying—something strange happens. The robot doesn't just get a little tired; it starts hallucinating, forgetting its own rules, or walking in circles until it gives up.

This paper, titled "The Long-Horizon Task Mirage," investigates exactly why this happens. The authors argue that as tasks get longer, AI agents don't just fail more often; they fail in completely different, more chaotic ways.

Here is the breakdown of their findings using simple analogies.

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1. The Problem: The "Long-Horizon" Trap

In the world of AI, a "short-horizon" task is like ordering a pizza. You say "pepperoni," they make it, you get it. Easy.

A "long-horizon" task is like planning a cross-country road trip. You have to book flights, rent a car, find hotels, check the weather, and navigate traffic. The paper calls this a "mirage" because while current AI models look amazing on short tasks, they seem to vanish into thin air when the task gets long. They don't just make a mistake; the whole plan collapses.

2. The Solution: HORIZON (The Diagnostic Tool)

The researchers built a new testing ground called HORIZON. Think of HORIZON as a giant, multi-level obstacle course for AI agents.

• The Track: Instead of just one race, they built tracks of increasing length.
  • Level 1: Walk 10 steps.
  • Level 2: Walk 20 steps.
  • Level 3: Walk 50 steps.
• The Twist: They tested this in four different "worlds":
  1. The Web: Browsing websites (like a digital shopper).
  2. The OS: Using a computer's operating system (like a file manager).
  3. The Database: Querying massive data tables (like a librarian).
  4. The Embodied World: Controlling a robot arm (like a physical worker).

They ran over 3,100 experiments with the smartest AI models available (GPT-5 and Claude-4) to see exactly where and why they broke.

3. The Findings: The "Seven Deadly Sins" of AI

The team discovered that when AI agents fail on long tasks, it's not random. They fall into seven specific categories of failure. Imagine these as the seven ways a hiker can get lost in a forest:

1. The Environment Disturbance (The Shifting Ground):

   • Analogy: You are walking a path, but the ground suddenly shifts, or a tree falls in front of you. The AI doesn't notice the change. It keeps walking into the tree, thinking the path is still clear.
   • Example: A webpage loads slowly, but the AI clicks a button that isn't there yet.

2. The Instruction Error (The Misheard Recipe):

   • Analogy: You tell a chef, "Make a cake, but no sugar." The chef hears "Make a cake, with sugar."
   • Example: The AI is told to "filter results," but it sorts them instead.

3. Catastrophic Forgetting (The Amnesia):

   • Analogy: You are writing a story. By page 50, you forget that the main character is a detective, so you suddenly make him a baker.
   • Example: The AI was told "don't buy anything over $200" at the start. By step 15, it forgets that rule and buys a $500 item.

4. False Assumptions (The Hallucination):

   • Analogy: You are in a dark room and assume the chair is in the corner, so you walk into it. It wasn't there.
   • Example: The AI assumes a website has a "search" button when it doesn't, so it tries to click on empty space.

5. Planning Error (The Bad Map):

   • Analogy: You want to drive to the beach, but your map says to turn left at the first gas station. You turn left, end up in a swamp, and never reach the beach.
   • Example: The AI tries to "checkout" before it has even "added the item to the cart."

6. History Error Accumulation (The Snowball):

   • Analogy: You make a tiny mistake at the start of a math problem (writing a '6' instead of a '9'). By the end, the answer is completely wrong because that one digit ruined everything.
   • Example: The AI clicks the wrong link early on. It keeps clicking links based on that wrong page, getting further and further off track.

7. Memory Limitations (The Overloaded Backpack):

   • Analogy: You are carrying a backpack. You keep adding items until it bursts, and you drop the most important item (the map) without realizing it.
   • Example: The conversation gets so long that the AI "forgets" the very first instruction because it's too far back in the chat history.

4. The Big Surprise: It's Not Just "Smarter" Models

The most important discovery is that making the AI bigger or smarter doesn't fix this.

• The "Breaking Point": Every model has a "breaking point." On short tasks, they are 90% accurate. But once the task gets long enough, their accuracy crashes to near zero.
• The Convergence: Interestingly, the super-smart models and the slightly-less-smart models fail at almost the exact same point. Being "smarter" just helps a little bit on short tasks; it doesn't solve the long-horizon problem.

5. The Verdict: We Need New Tools, Not Just Bigger Brains

The authors conclude that we can't just wait for AI to get "smarter" on its own. To fix long-horizon tasks, we need to change how the AI thinks, not just how big its brain is.

• Better Planning: We need AI that checks its map while it walks, not just at the start.
• Better Memory: We need AI that can carry a "backpack" that doesn't burst, keeping the most important rules safe.
• Better Self-Correction: We need AI that notices when the ground has shifted and stops to re-evaluate.

Summary

This paper is a wake-up call. It tells us that while AI agents are great at short, simple tasks, they are currently mirages when it comes to long, complex jobs. They look capable, but they break down systematically.

The authors have provided a diagnostic toolkit (HORIZON) to help developers see exactly where the robot is getting lost (is it forgetting? is it hallucinating?) so they can build better, more reliable AI for the future.

Technical Summary

1. Problem Statement

Large Language Model (LLM) agents demonstrate strong performance on short- and mid-horizon tasks but suffer from systematic breakdowns when executing long-horizon tasks (extended, interdependent action sequences).

• The Gap: Current research lacks a principled, cross-domain framework to diagnose where and why these failures occur. Existing benchmarks are often domain-specific, use inconsistent definitions of "horizon," and focus primarily on aggregate success rates rather than trajectory-level failure attribution.
• The Challenge: Long-horizon behavior is inherently domain-dependent. A task requiring 10 steps in a web environment might be trivial, while 10 steps in an embodied robot environment could be impossible. Furthermore, errors in long-horizon tasks are not merely additive; small per-step errors compound, leading to catastrophic trajectory-level failures.
• Core Research Questions:
  1. Where do agents break down as task horizons increase?
  2. Why do these failures emerge (what are the dominant failure modes)?

2. Methodology: The HORIZON Framework

The authors introduce HORIZON (Holistic Observations for Reasoning and faIlure analyZis in lOng-horizoN agents), a cross-domain diagnostic benchmark designed to systematically construct tasks and analyze failure behaviors.

A. Defining Task Horizons

To separate task complexity from agent inefficiency, HORIZON defines horizon using two layers:

1. Theoretical Metrics (Agent-Independent):
   • Intrinsic Horizon ($H^*$): The minimum number of effective actions an optimal policy needs to complete a task.
   • Compositional Depth ($s$): The number of nested sub-goals or conditional branches, reflecting planning complexity.
2. Technical Implementation (Controlled Extension):
   • Depth Extension: Incrementally adds intermediate, non-skippable steps between existing actions (e.g., adding permission checks in OS tasks).
   • Breadth Extension: Combines multiple independent baseline tasks into a single composite workflow, requiring coordination and state tracking across sub-tasks.

B. Failure Attribution Taxonomy

The authors propose a 7-Category Failure Taxonomy grounded in Failure Mode and Effects Analysis (FMEA). These categories are orthogonal dimensions, meaning a single trajectory can exhibit multiple failure types simultaneously.

• Process-Level Risks (PFMEA): Failures arising during sequential rollout.
  • Environment: Disturbances or inability to detect state changes.
  • Instruction: Ill-defined instructions or partial understanding.
  • Planning Error: Incorrect sub-planning or action ordering.
  • History Error Accumulation: Compounding of early mistakes.
• Design-Level Risks (DFMEA): Limitations of the engineered agent architecture.
  • Catastrophic Forgetting: Losing constraints or knowledge despite them being in context.
  • False Assumptions: Relying on hallucinated facts or incorrect priors.
  • Memory Limitations: Context window overflow or lossy summarization.

C. Evaluation Pipeline

• Models Tested: State-of-the-art (SOTA) models including GPT-5 variants (mini) and Claude-4 (Sonnet).
• Domains: Four representative agentic domains: Web (WebArena), Operating Systems (AgentBench), Databases (MAC-SQL), and Embodied (Isaac Sim).
• Scale: 3,100+ trajectories collected across 700+ tasks.
• Automated Analysis: A Trajectory-Grounded LLM-as-a-Judge pipeline was developed to attribute failures to the 7 categories. This was validated against human annotators, achieving strong agreement (Human-Judge $\kappa = 0.84$; Inter-annotator $\kappa = 0.61$).

3. Key Results

A. Performance Degradation Patterns

• Non-Linear Collapse: Performance does not degrade linearly with horizon length. Agents remain relatively stable at low horizons but experience a sharp, abrupt drop in success rates once a domain-specific "breaking point" (transition region) is reached.
• Domain Variance:
  • Web: Collapses at very low compositional depths ($s$).
  • OS & Database: Sustain moderate performance until later extension levels.
  • Embodied: Degrades steeply even with minimal increases in $s$.
• Model Convergence: Once agents enter the long-horizon failure regime, performance gaps between different model families (e.g., GPT-5 vs. Claude-4) narrow significantly, suggesting that scaling base models alone does not solve long-horizon reliability.

B. Failure Composition Shift

As the horizon increases, the nature of failures shifts structurally:

• Dominant Modes: Planning Errors (specifically sub-planning) and Memory-Related Failures (Catastrophic Forgetting, Memory Limitations) become the dominant causes of failure.
• Specific Findings:
  • Embodied & Database: Overwhelmingly dominated by Planning Errors (94.9% and 79.3% of failures, respectively).
  • Web: Dominated by Planning Errors but also shows significant Environment and Memory Limitation failures.
  • OS: Exhibits the most diverse failure profile, with significant contributions from Instruction, Environment, and Planning errors.
• Model Differences:
  • GPT-5: Failures are dominated by Planning Errors (64.9%) and Memory Limitations (18.3%).
  • Claude-4: Shows higher susceptibility to Environment (32.5%) and Instruction (16.5%) failures, with near-zero Memory Limitation failures, suggesting better context retention but higher sensitivity to environmental noise.

4. Key Contributions

1. HORIZON Benchmark: The first cross-domain diagnostic benchmark that systematically constructs task families with controlled horizon extension to study degradation patterns.
2. 7-Category Taxonomy: A structured, FMEA-grounded taxonomy for failure attribution that distinguishes between process-level execution errors and design-level architectural limitations.
3. Scalable Diagnosis Pipeline: A validated LLM-as-a-Judge system for reproducible, large-scale failure attribution, reducing the need for expensive human annotation.
4. Empirical Insights: Evidence that long-horizon failure is a structural shift in failure composition, not just a drop in success rate, and that model scaling alone is insufficient to resolve these issues.

5. Significance and Implications

• Beyond Scaling: The paper argues that simply increasing model size or training on longer traces will not solve long-horizon failures. Instead, the field must focus on method-level improvements in:
  • Hierarchical Planning: Better sub-goal decomposition and verification.
  • Memory Mechanisms: Explicit constraint tracking and retrieval to prevent catastrophic forgetting.
  • Execution Control: Real-time plan verification and repair mechanisms.
• Benchmarking Shift: The authors advocate for moving away from single-point success rates toward horizon-aware evaluation curves that report performance transitions and attributed failure types.
• Real-World Relevance: The findings align with observed failures in real-world deployments (e.g., OpenClaw), where agents fail due to forgetting constraints or misinterpreting environment states over time, validating the diagnostic utility of the HORIZON framework.

In conclusion, HORIZON provides a foundational tool for the community to move from observing that agents fail on long tasks to understanding how and why they fail, enabling targeted architectural interventions for building more reliable agentic systems.