A recent study by LangChain reveals that artificial intelligence agents struggle to perform at human levels when overwhelmed by tools and instructions. Findings showed that their performance significantly dropped as agents were tasked with more responsibilities. For instance, one model’s effectiveness plummeted to just two percent when handling over seven domains. LangChain’s tests involved an internal email assistant tasked with responding to customer queries and scheduling meetings, with 30 tasks each run three times. The results indicate that more context leads to worse instruction following, highlighting the challenges of managing multi-agent systems. LangChain aims to further explore how to enhance agent performance in complex environments.
Why do we care?
The study confirms that adding more tools and responsibilities doesn’t enhance AI performance; it actively degrades it. This aligns with known cognitive overload issues in human workers but presents a more severe drop-off in AI effectiveness. More tools is not the answer.
For IT service providers and businesses integrating AI-driven automation, this underscores the importance of designing systems that manage complexity effectively rather than assuming AI can simply “scale up” to human-like multitasking. Now, humans also struggle with overload, so comparing AI to human multitasking without benchmarking human error rates might be misleading.
AI isn’t a magic bullet for automation—especially for complex workflows. Investments should focus on structured delegation, better context management, and optimized AI-agent orchestration rather than assuming that a single AI can handle everything. This could shape AI strategy in automation-heavy industries like customer service, IT operations, and enterprise software.