Context Quality Predicts AI Agent Reliability, Study Finds.

Research highlights that AI agent failures are often rooted in the quality of their operating context, rather than the agent itself. This context encompasses instructions, tools, memory, retrieved knowledge, and guardrails. When this context is poorly engineered, agents are prone to issues such as drifting, hallucinating, misusing tools, ignoring constraints, and becoming vulnerable to injection attacks. The ProofAgent-Harness, an open-source evaluation infrastructure, introduces a method to measure context-engineering quality across seven criteria, including role clarity, guardrail coverage, and grounding sufficiency. A controlled study demonstrated that these context quality scores consistently predict corresponding behavioral outcomes. For instance, grounding sufficiency predicts hallucination resistance, and guardrail coverage predicts manipulation resistance, establishing context measurement as a crucial preflight signal for agent reliability and governance.