CAFE(S): Your Agent Is Only as Good as Its Context

As organizations delegate increasingly complex software tasks to AI agents, engineering leaders often attribute system failures to model capabilities or harness orchestration. However, even state-of-the-art models degrade rapidly when operating on unclear, incomplete, or stale inputs. We present CAFE(S), a diagnostic quality framework that evaluates assembled context across five core dimensions: Clarity, Actionability, Fidelity, Efficiency, and Security. CAFE(S) provides a shared vocabulary for identifying recurring context failures—from ambiguous requirements and impossible tasks to excessive noise and unsafe inputs—and for reasoning about how those failures affect human-agent work. We argue that context quality is a first-class engineering concern that can be deliberately designed, evaluated, and maintained. Organizations that systematically strengthen the information environments in which agents operate will be far better positioned to realize their full potential.

Authors

• Brian Houck
• Max Kanat-Alexander
• Eirini Kalliamvakou
• Margaret-Anne Storey
• Nicole Forsgren