May 10, 2026 · 25 min · 13 segments
Context windows are powerful — but finite, and surprisingly easy to overwhelm. When an AI agent is tackling a long, complex task, the information it needs has to fit inside that limited real estate…
Hi, welcome to Linear Digressions.
So if you haven't heard the first three, don't worry, you're gonna get plenty out of this one.
But if you have heard the first three, or especially the last one, this is gonna be a really interesting episode.
They can take actions and use tools, and everything that they have to know to do their job has to fit inside of their context window, which is a limited amount of real estate.
Sometimes it can be a lot of real estate, but it's not an unlimited amount.
And in particular, what we looked at last time was this idea that even if you have a very large context window, it's really the beginning and end that are gonna be the most useful.
Stuff that happens in the middle of that context window tends to get lost for architectural reasons.
And so between both that lost in the middle and the fact that context is finite, it introduces this overall challenge of how to manage the memory and that context of an agent so that it has the information that it needs to effectively do its job, especially over really long time horizons or long-running complex tasks where there's a lot of things going on at the same time.
That is what we are going to talk about today, the memory management of AI agents, starting with some of the foundational concepts from computing, but then pretty quickly getting into things like Claude Code, which you might know from your day-to-day work.
You are listening to Linear Digressions.
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Hi, welcome to Linear Digressions.
So if you haven't heard the first three, don't worry, you're gonna get plenty out of this one.
But if you have heard the first three, or especially the last one, this is gonna be a really interesting episode.
They can take actions and use tools, and everything that they have to know to do their job has to fit inside of their context window, which is a limited amount of real estate.
Sometimes it can be a lot of real estate, but it's not an unlimited amount.
And in particular, what we looked at last time was this idea that even if you have a very large context window, it's really the beginning and end that are gonna be the most useful.
Stuff that happens in the middle of that context window tends to get lost for architectural reasons.
And so between both that lost in the middle and the fact that context is finite, it introduces this overall challenge of how to manage the memory and that context of an agent so that it has the information that it needs to effectively do its job, especially over really long time horizons or long-running complex tasks where there's a lot of things going on at the same time.
That is what we are going to talk about today, the memory management of AI agents, starting with some of the foundational concepts from computing, but then pretty quickly getting into things like Claude Code, which you might know from your day-to-day work.
You are listening to Linear Digressions.
