AI Agent Reliability in 2025: Failure Modes and Success Rates of Long-Horizon Tasks

July 11, 2026Open Access

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Key Points

The research aims to assess the reliability of AI agents in completing long-horizon tasks by identifying failure modes and measuring success rates.
Analysis of various AI agents tasked with long-horizon objectives in controlled scenarios
Assessment of failure modes to determine contributing factors to unreliability
Statistical evaluation of success rates across diverse task conditions
Identified key failure modes affecting AI reliability, including decision-making errors and resource allocation issues
Success rates for AI agents decreased significantly over extended task durations, indicating reliability challenges
Recommendations provided for enhancing AI performance in long-horizon tasks based on identified failures

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Research article evaluates AI agent reliability, revealing success rates and failure modes in long-horizon tasks, suggesting avenues for improvement.

Research article: AI Agent Reliability in 2025: Failure Modes and Success Rates of Long-Horizon Tasks

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Ивченко et al. (2026) studied this question.

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