Agent Fatigue
A pattern where AI agent performance degrades on tasks requiring sustained attention or processing very long contexts—analogous to human cognitive fatigue but caused by context window limitations, attention mechanism degradation, and cumulative error propagation in multi-step workflows. Symptoms include declining accuracy in later steps of long workflows, increased hallucination rates in long conversations, and inconsistent behavior when processing large document sets. Mitigation strategies include task chunking, periodic context summarization, and checkpoint-based workflows that reset context at defined intervals.
Written by Max Zeshut
Founder at Agentmelt · Last updated Oct 26, 2026
Example
An AI legal agent reviewing a 200-page contract achieves 96% clause extraction accuracy on the first 50 pages but drops to 88% on pages 150-200 as the accumulated context degrades attention quality. Splitting the review into 50-page chunks with intermediate summarization maintains 95%+ accuracy throughout.
Frequently asked questions
- How do you detect agent fatigue?
- Monitor accuracy metrics across task position—compare performance on the first items in a batch versus the last. Track hallucination rates as conversation length increases. Set up automated quality checks at different points in long workflows. If accuracy drops beyond a threshold as context grows, implement chunking or context reset strategies.