--- title: Do you really need another framework? | daily.dev description: A newsletter piece introduces the CAFE(S) framework for evaluating the quality of context given to AI agents across five dimensions: Clarity, Actionability,... canonical: https://daily.dev/posts/do-you-really-need-another-framework--fmzszu8sn twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Do you really need another framework? | daily.dev og:description: A newsletter piece introduces the CAFE(S) framework for evaluating the quality of context given to AI agents across five dimensions: Clarity, Actionability,... og:url: https://daily.dev/posts/do-you-really-need-another-framework--fmzszu8sn og:image: https://api.daily.dev/og/posts/fmZSZU8sn.png og:image:alt: Do you really need another framework? og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Do you really need another framework? **[Engineering Enablement](https://daily.dev/sources/engineering_enablement)** · 6 min read · 0 upvotes · 0 comments ## Summary A newsletter piece introduces the CAFE(S) framework for evaluating the quality of context given to AI agents across five dimensions: Clarity, Actionability, Fidelity, Efficiency, and Security. Rather than competing with existing measurement approaches, it argues CAFE(S) fills a gap none of them cover: whether the assembled context an agent works from is actually good. It walks through how CAFE(S) relates to DORA, SPACE, DX Core 4, EngThrive (outcome metrics), retrieval/RAG evals (partial overlap on fidelity and efficiency), OWASP (security-only focus), context/harness engineering guidance (prescriptive vs evaluative), and TRUCE (code quality's closest analog). The conclusion is that these frameworks answer different questions and can coexist rather than compete. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the CAFE(S) framework for evaluating AI agent context quality? CAFE(S) is a framework evaluating the quality of context given to AI agents across five dimensions: Clarity, Actionability, Fidelity, Efficiency, and Security. It asks whether the information handed to an agent is good enough for the task, independent of how that context was built, filling a gap left by outcome metrics, retrieval evals, OWASP security guidance, and context engineering practices. _Teams evaluating agentic workflows can track frameworks like this one alongside DORA and OWASP on daily.dev._ ### How is CAFE(S) different from retrieval evals in RAG systems? Retrieval evals score only the passages a retriever fetched, covering roughly the Fidelity and Efficiency dimensions of CAFE(S), while an agent actually reasons over the full assembled context including the human prompt, an AGENTS.md file, tool output, prior turns, and carried-forward memory. Since most of that never passes through a retriever, retrieval evals miss most of what the agent actually reads. _Developers debugging agent behavior can compare context and retrieval evaluation approaches on daily.dev._ ### How does CAFE(S) relate to OWASP's LLM security guidance? OWASP goes far deeper on security risk than CAFE(S), since security is only one of CAFE(S)'s five pillars while OWASP is dedicated entirely to security guidance. CAFE(S) instead places security inside a broader context quality model, arguing security and usefulness aren't always separable: oversharing context can be both a security failure and an efficiency failure simultaneously. _Security-conscious teams weighing usefulness against risk in agent context can follow this discussion on daily.dev._ ## Similar posts on daily.dev - [You launched an AI assistant. 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