Field Guide: Common Failure Modes in LLM and AI Agent Systems Explained

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A technical guide published on DEV Community outlines the most common failure modes affecting large language model (LLM) and AI agent systems as of 2025–2026. The guide is structured around three categories: model-level issues such as hallucination and context degradation, agent-level problems including compounding errors and tool misuse, and system-level risks like prompt injection and model drift. It identifies three root causes behind most failures: LLMs are probabilistic predictors, attention is a finite resource, and models cannot distinguish trusted from untrusted input tokens. Each section follows a consistent format covering what goes wrong, why it happens, how to fix it, and a checklist for teams. The guide draws on research from Anthropic, Meta AI, Cognition, and security researchers to support its recommendations.

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