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AI context is its limited working memory. It is how much information the AI can handle at once.
Once you push beyond that limit, the AI has to compress what it knows. Details start to disappear. Companies often miss this, load far too much into one context, and get crappy results back.
The problem is easy to recognize once you know what you are looking for.
At a networking event, someone asked me whether it would be cool to take a thousand sales-call transcripts, stuff them into an AI, and have it find patterns.
It would be cool. One AI agent cannot carry all of that context at once and still hold onto every useful detail.
There are ways to do that kind of analysis. The work needs to be divided instead of handed to one overloaded agent.
Split agents by job
I often suggest thinking about an AI agent like an intern. An intern needs the information required for the job in front of them. Giving them everything the company knows does not make that job clearer.
If one intern is organizing your files, they do not need your company values to sort the folders. That extra context takes up room without helping with the assigned task.
The same idea applies to AI agents. Give each one a specific job, then give it the context needed for that job. When the work is larger, split it into separate jobs rather than stuffing every source into one place.
This keeps each agent focused on an amount of information it can handle.
Before loading more material into an agent, ask what job it is doing. Keep the context tied to that job. A file-organizing agent can organize files, and the larger analysis can be divided into work that fits.