The 3 Roles of Context for AI Agents - NN/G

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September 18, 2026 2026-09-18
Advanced AI users have AI agents execute complex and laborious tasks for them. But the leverage they can get largely depends on the context the AI has access to. Without the right context, AI agents can produce generic or problematic outputs that don’t reflect users’ needs, business goals, or a product’s brand.
Curating the right context now matters even more than writing a good prompt.
In a recent study with Claude power users — people who rely on the agent daily for substantial professional work — every participant had built a context library: a collection of files, databases, and live connections that Claude drew on alongside whatever the user typed in the prompt.
The libraries differed in form, but the information in them consistently played one of 3 roles. These roles are not specific to Claude or to power users: any AI agent that works from files, memory, or live connections needs information of all three kinds, and any user who supplies it is deciding, knowingly or not, which role it plays.
The 3 roles that AI context can play are:
Which role a piece of information should play was decided sometimes by Claude and sometimes by the user. Some files (agent skills, for instance) included a short description of when they applied, so Claude could recognize on its own that a particular file in the library was relevant to the current interaction and use it. In other cases, the user decided: they would upload the information directly or explicitly point Claude to information stored in the library and ask it to use it for the current task.
To illustrate the differences between these roles, take the task of writing a research report. The table below highlights which inputs play which roles.
The best practices that shape what a good report entails
The to-do list the researcher is using to manage their work and the research data they are working from
The recent emails with the client about the project and a transcript of a call with a colleague discussing the main findings
Note that in this example, the research report itself is the output of the task, not a form of context. However, the report could later serve as context for similar future projects if it were a good example of a properly prepared research report.
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The role that one piece of context information plays can change. A Slack message, for example, may start out as one item in the ambient stream pulled in through an MCP connection. Once the user points the AI at that message for the current task, it becomes local context. Or an article that a user heavily references as local context for a set of specific tasks could evolve into global context if the user archived it so that it became a reference across interactions. Certain information might even play multiple roles at the same time.
Global context is meant to last. It guides the agent’s high-level reasoning and priorities, and it rarely changes.
Here are a few examples of global context, each from participants in our study:
Global context can be powerful because it relieves the user of having to specify these details in each new conversation.
Local context is information the AI uses that is directly related to the user's current work. Here are some examples of local context from our study participants:
Local context generally lives either in a specific file that the AI can directly access and edit (such as a markdown file) or in an external database. Some of it (task lists, change logs, decision logs) is written and kept current by Claude itself, not just read. However, it is not the direct artifact or material the user is currently working on — it supports it, helping the AI understand and communicate its priority and progress.
Ambient context is the messiest and largest type of context. It consists of the raw, uncurated information streams that surround the user’s work — emails, meeting transcripts, chat messages — and that were not scoped to a task in advance. This information is valuable precisely because it is unfiltered: it captures what shapes the user’s priorities and sense of what good work looks like, without requiring the user to explicitly document it.
One participant in our study described it this way:
“[Some documents I create are] certainly helpful, but I want them in the moment, and then I'm never going to need [to use] them again. But they do need to remain around for context … like a Google Doc, that I then used as a transcript for a phone call, where I [then] made edits to it, and talked about it in a different way”
In other words, he did not want to curate his call transcripts or his notes into global or local context, but he did want Claude to keep them in view. Like ambient noise, these streams are always on, never organized, and still shape how the user thinks, so the AI should take them into account.
Participants in our study made no effort to define, inspect, clean, or format ambient context. Most of it came from continuously changing sources of information (such as email or Reddit posts) and reached Claude through MCP connections. More information is not always better, but participants had no time to curate what mattered from these streams and file it as global or local context. So they handed Claude the whole stream and hoped it would keep up with them.
Examples of ambient context in our study included:
Some of these data came from sources that were easy for Claude to access through MCP connections. Other (like Shopify or certain calendars) required users to create workarounds to grant Claude access. Yet participants in our study were willing to figure out a solution and maintain it because of the great value they placed on providing Claude with as much amb