As AI coding agents move from code suggestions toward autonomous software-development tasks, researchers from DX, Capital One, GitHub, the University of Victoria and Google are proposing a framework for a less visible part of the AI stack: the quality of the context agents receive. Published in ACM Queue, CAFE(S) defines five dimensions—Clarity, Actionability, Fidelity, Efficiency and Security—for evaluating whether the information surrounding an AI agent is fit for the task.
AI coding agents are increasingly being asked to do more than autocomplete a function or explain a block of code. They can investigate issues, modify repositories, run tests, create pull requests and operate across parts of the software development lifecycle. That shift is putting greater pressure on the information these systems use to make decisions.
The newly published CAFE(S) framework argues that model capability is only one part of the reliability equation. Even advanced models can struggle when their inputs are ambiguous, incomplete, outdated, excessively broad or inappropriate to access. The researchers describe context quality as a distinct engineering concern that organizations can deliberately design and maintain.
CAFE(S), whose name represents Clarity, Actionability, Fidelity, Efficiency and Security, is intended as a shared vocabulary rather than another AI model, retrieval system or agent runtime.
Clarity addresses whether an agent can interpret a request in the way the human intended. A task may appear obvious to its author while leaving important assumptions unstated for an AI system.
Actionability examines whether an agent has a defined objective, meaningful constraints and a way to determine when the work is complete. A technically clear request can still fail if the agent does not know what outcome or boundaries should govern execution.
Fidelity focuses on whether the information is accurate when the agent receives it. This becomes particularly important in large software organizations where documentation, architectural decisions and repository instructions can become outdated or contradictory. The researchers argue that agents can confidently reason from information that was once correct but no longer describes the current system.
Efficiency addresses the amount and relevance of context supplied to an agent. More information is not necessarily better. Feeding an entire repository into a task that requires only a particular function can increase token consumption while making important information harder for the model to identify.
The fifth dimension, Security, asks a different question: whether the agent should have access to the information at all. The researchers separate it from the first four dimensions because security concerns appropriate access, compliance and safety rather than simply whether context improves task performance.
That distinction is becoming more significant as AI coding systems gain access to repositories, tools and development environments. GitHub, for example, has expanded Copilot from an assistive coding product toward agentic workflows in which agents can work asynchronously on issues and produce pull requests for human review. GitHub has also described context engineering as an important part of building more reliable AI workflows.
The broader market is moving in the same direction. Gartner said in May 2026 that enterprise AI coding agents were entering a new phase of expansion as vendors moved from code assistance toward agent-driven software development across the software delivery lifecycle. Gartner projected that by 2027, more than 65% of engineering teams using agentic coding would treat traditional IDEs as optional, shifting more control, governance and validation toward automated platforms.
Developer adoption also shows why reliability and context management are becoming important. Stack Overflow’s 2025 Developer Survey reported that 80% of developers were using AI tools in their workflows, while only 29% said they trusted the accuracy of AI output. The survey also found that 66% spent more time fixing AI-generated code that was “almost right.”
CAFE(S) therefore addresses an emerging layer between human intent and model execution. Traditional knowledge management asks whether information exists and is maintained. Information retrieval asks whether relevant information can be found. Context engineering goes another step by asking whether the information assembled for a particular agent task is actually suitable for that task.
The framework is intentionally not presented as a completed benchmarking system. The researchers say additional work is required to develop reliable measurements at scale and establish how improvements in context quality affect developer experience, software delivery and organizational outcomes.
That limitation is important. CAFE(S) does not establish that every agent failure is caused by poor context, nor does it provide a numerical score that can independently predict whether an AI coding agent will succeed. Instead, its potential value is organizational: it gives engineering and platform teams a common way to identify whether failures originate in requirements, outdated knowledge, excessive information or unsafe access.
As AI agents take on larger portions of software engineering, the competitive question may increasingly extend beyond which company has the strongest model. Retrieval quality, repository structure, documentation, permissions, tool access and context orchestration can all shape what an agent is actually capable of doing.
CAFE(S) puts those information conditions into a framework that engineering organizations can inspect before assuming the underlying model is the problem.
Market Landscape
The enterprise AI coding-agent market is moving from assisted development toward increasingly autonomous software engineering. Gartner estimated the annualized enterprise AI coding-agent market at roughly $9.8 billion to $11 billion as of April 2026, while describing context orchestration, autonomous execution and governance as central characteristics of the evolving category.
The competitive environment includes platforms such as GitHub Copilot and other agentic development systems that increasingly combine large language models with repository retrieval, tools, execution environments and workflow automation. This makes context infrastructure an important complement to model performance.
CAFE(S) fits into that transition by treating context as an engineering artifact rather than an incidental input to an LLM. Its next challenge will be turning the five qualitative properties into repeatable measurements that can be applied across organizations and agent architectures.
Top Insights
- CAFE(S) shifts attention from model capability alone toward the quality, relevance and safety of information supplied to AI coding agents.
- The framework separates useful context into five dimensions: Clarity, Actionability, Fidelity, Efficiency and Security.
- Stale documentation and excessive context can undermine agent performance even when organizations use highly capable frontier models.
- Gartner expects enterprise coding agents to expand across the software lifecycle, increasing the importance of context orchestration and governance.
- CAFE(S) is currently a diagnostic framework, not a validated numerical benchmark for predicting agent performance.
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