The Root Cause of AI Agent Failures Lies in Context
New research proposes a "context scoring" framework that evaluates the operating environment of AI agents across seven dimensions, including role clarity, tool description, and factual support. It finds that the main reason for agent failures is the lack of good instructions, tools, evidence, memory, or safety rules. This score is unrelated to the agent's actual behavior score, but converting vague instructions into structured ones significantly improves the performance of the same model in 300 tests and 7,500 interaction rounds. More factual support reduces hallucinations, clearer tool descriptions improve tool usage, but adding safety rules may cause agents to become overly cautious, revealing a real trade-off.
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