AI fatigue: what happens when product teams can't keep up with their own agents

As AI integration into software engineering accelerates productivity, it also brings challenges like AI fatigue, where engineers face cognitive burdens from supervising multiple AI agents simultaneously. Guillaume Moigneu, Field CTO at Upsun, highlights the strain of context-switching and the constant need for reviewing AI-generated code, which often arrives unpredictably and demands immediate attention. This fatigue is compounded by the trust issue, as engineers remain accountable for AI outputs without the benefit of understanding the intent behind them. While some teams attempt to mitigate these issues through additional processes or automation, the structural problem persists, as workflows have not evolved to match the capabilities of the tools. This imbalance can lead to team bottlenecks, where the rapid output of proficient engineers overwhelms the team's capacity to review and integrate new code, potentially exacerbating existing silos and creating resentment. AI fatigue signals that existing workflows are outdated and need redesigning to accommodate modern software development practices, focusing on reducing the supervisory load on humans and enhancing problem-solving efforts.

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