I've been building long-running agentic workflows and kept hitting the same problem: the AI forgets instructions from earlier in the conversation, rushes to produce output, and skips boring middle steps.
The research explains why:
"Lost in the Middle" (Stanford 2023) showed a 30%+ performance drop when
critical information is in the middle of the context window. Accuracy is
high at the start and end, drops in the middle. Exactly like working memory
overflow.
"LLMs Get Lost in Multi-Turn Conversation" (Laban et al. 2025) showed that
instructions from early turns get diluted by later content. The more turns,
the worse the recall.
65% of enterprise AI failures in 2025 were attributed to context drift
during multi-step reasoning.
The parallel to ADHD executive dysfunction isn't metaphorical. Dense local
connectivity in transformer attention mirrors the "intense world" theory of
neurodivergent processing. Both produce: strong pattern recognition + weak
executive control over long sequences.
The fixes map too. "Echo of Prompt" (re-injecting instructions before
execution) is the AI equivalent of re-reading the question before answering.
Task decomposition into small steps reduces overwhelm. External
verification prevents self-reported false completion.
Has anyone else noticed this pattern in their agentic builds? Curious what
scaffolding techniques others are using for long-running workflows.
[link] [comments]