Agent Failure Starts with Context
rohanpaul_ai · x · 2026-07-20
This paper argues that AI agent failures are often not due to a "broken model," but rather a breakdown in context configuration first. The authors propose using an independent context score to evaluate the quality of the agent's environment. In their open-source framework, ProofAgent-Harness, they break down context into 7 scoring dimensions: role clarity, tool description, factual support, rule consistency, system security, injection defense, and token efficiency.
In an experiment involving 300 tests and 7,500 turns, they kept the model fixed and only adjusted the context configuration. Results showed that more structured context significantly improves performance: more factual support leads to fewer hallucinations, clearer tool descriptions lead to better tool usage, and stronger safety guardrails improve resistance to manipulation. However, adding too many safety rules can make the agent overly cautious, highlighting a real trade-off between reliability and task completion.
Related event: Study: Context Engineering is Key to AI Agent Success(2 posts)→
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