What Is Context Management, and Why It Matters When You Work With AI Agents

The moment I realized context was everything

I did not really understand what context management was until I started building my own agents. Up until then, working with AI felt simple. Open the chat, type a prompt, get an answer, move on. The second I tried to build something that actually ran on its own, everything changed.

One of the projects that made this click for me was a technical qualification agent. The idea was to have it review incoming requests, qualify whether the request was actually possible, and then write out the technical details needed to make it happen. On paper it was a clean, well scoped project. In practice, it kept losing track of things. It would start a qualification, go read some documentation, come back, and suddenly forget half of what the original request even said. The more tools I gave it, the more confused it got.

Around the same period, I was building a Head of Marketing agent to help me run AI at Work Academy. That one was even more eye opening. I wanted it to understand my brand, my voice, my audience, the content we had already published, and the goals for the month. I basically wanted it to onboard like a new marketing hire. The first version tried to hold all of that in a single execution, and within a few hours it was drifting. It would suggest ideas that contradicted posts from the week before. It would forget what tone we were going for. It would repeat itself.

The problem was not the model. The problem was context. How much the agent knew at any given moment, where that information lived, what stayed and what got dropped. That was the moment I understood something that most people working with AI eventually bump into. The model is only as good as the context you give it, and managing that context is a real skill.

So what is context management, really?

Context management, sometimes called context engineering, is the practice of deciding what information an AI model gets to see, when it sees it, and how it moves in and out of the conversation. It is everything that lives inside the model's attention at any given moment. The system prompt, the chat history, the documents you uploaded, the tools the agent can use, the notes it keeps about you, and the outputs of the actions it already took.

A good way to think about it is this. A model is like a colleague with brilliant reasoning but no long-term memory. Every time you talk to it, you are basically onboarding that colleague all over again. Context management is how you decide what to put in front of them, what to leave out, and how to keep the important stuff handy without drowning them in noise.

Anthropic describes it as the discipline of finding the smallest set of high signal tokens that maximize the chance of getting the result you want. In plain words, give the model what it needs to do the job well, and not a single word more.

When did this start being a big deal?

For a long time, the main conversation around working with AI was prompt engineering. How do I phrase my question so the model gives me the best answer? That made sense when we were mostly using AI for one off requests. You write a prompt, you get a reply, you move on.

But the second we started building agents that run longer, use tools, make decisions, and handle multi step tasks, the game changed. Now the model is not just answering one question. It is holding a running conversation, reading files, calling tools, and making choices based on everything it has seen so far. Suddenly the question is not just how you phrase a prompt. The question is what is in the model's head while it is working.

Anthropic published a piece in late 2025 called Effective Context Engineering for AI Agents where they called this the natural next step after prompt engineering. OpenAI has been publishing similar guidance in their Agents SDK documentation, with entire sections dedicated to session memory, context trimming, and long term memory notes. And on X, builders like @trq212 have been pointing out that before Claude Code, people assumed context windows would just keep growing until they fit your entire codebase. But that is not how work actually happens. You do not need to remember everything. You just need to know how to find what matters.

Why context becomes critical the moment you add agents

Here is a concept that made everything click for me. It is called context rot. As more and more tokens pile up in the model's window, its ability to actually recall and use that information gets worse. Even when the model technically supports a million tokens, it does not mean it will remember every detail equally well. The more you stuff in, the harder it is for the model to focus on what actually matters.

This is why agents are different from a simple chat. A chat is usually short. An agent might run for an hour, call fifteen tools, read five documents, and make decisions at every step. Without active context management, three things start happening. The model loses the plot because the relevant instructions got buried under tool outputs. The model starts hallucinating because it cannot reliably retrieve something it saw twenty steps ago. And your bill goes up because every extra token costs money and slows things down.

Anthropic calls this the attention budget. You have a limited number of tokens the model can actually pay attention to. Every token you spend on junk is a token you are not spending on the task.

What good context management actually looks like

Let me give you a few examples that show the difference. These are all real patterns used in production agents today.

The first one is compaction. When a conversation starts getting too long, the agent summarizes everything that happened so far into a short, high quality note and then starts a fresh window with that summary at the top. Anthropic uses this heavily in Claude Code. It is the reason the agent can work on a big task for hours without forgetting the beginning.

The second is just in time retrieval. Instead of dumping every document the agent might need into the context up front, the agent holds lightweight references. A file path. A link. A query name. It only loads the actual content into the window when it is actually about to use it. This is how humans work too. You do not carry every file at your desk just in case. You open them when you need them.

The third is structured notes. The agent writes down what it learned in a persistent place outside the context window, like a memory file or a scratchpad. Then it loads only the relevant notes back in when it needs them. OpenAI's Agents SDK supports this through session memory and long term memory notes.

The fourth is multi agent architectures. Instead of one agent trying to hold all the context, you split the work across specialized subagents. Each subagent gets only the context it needs for its piece. The main agent coordinates and keeps the overall picture. This keeps every window clean and focused.

The fifth is context trimming. The simplest one. You just keep the last few turns of the conversation and drop the rest. Not always the best option, but sometimes it is exactly what you need.

The sixth is prompt caching. Both Anthropic and OpenAI let you mark large, stable pieces of context, like a system prompt, a knowledge base, or a long document, so the model only has to process them once and reuses them on later calls. This does not change what the model sees, but it makes every call faster and cheaper. For agents that run often against the same background knowledge, caching is a game changer. Honestly, there is enough depth in caching alone to justify its own post, so consider this a teaser. I will probably write a dedicated piece on it soon.

There is no single technique that solves this

If you take one thing from this post, let it be this. There is no silver bullet. Context management is not a fixed recipe or a single method you have to nail. It is a set of tools you combine depending on the job.

A short chat with a model to draft an email? You probably do not need anything fancy. Just write a clear prompt and go.

An agent running for two hours analyzing your spreadsheet and writing a report? You will want compaction and just in time retrieval.

A customer support agent that needs to remember a user's history across weeks? You will want structured memory notes and maybe a separate summary for each conversation.

A coding agent working across a big codebase? You will want file path references, search tools, and careful selection of what gets loaded into the window.

The teams that get the most out of AI are the ones who look at the problem and ask which combination of techniques fits best. Not the ones chasing the latest model or the biggest context window.

Context management will keep evolving with the models

One thing I find genuinely exciting is that this space is moving fast. Models are getting better at handling longer windows without losing focus. Tools are getting smarter about memory. Agent frameworks are building compaction and note taking right into the default behavior.

Anthropic's most recent guidance on long running agents already goes beyond single window tricks. They talk about agent harnesses that manage context across multiple sessions, pass state through files, and hand off tasks between subagents cleanly. OpenAI is moving in a similar direction with their multi agent workflows and the Codex plugins directory.

What this means for you and me is that the techniques that work well today will not be the same techniques that work well a year from now. Some of what feels essential now will be baked into the tools and we will stop thinking about it. New patterns will show up that we have not even imagined yet. Context management is not a fixed recipe. It is a moving target, and the skill is really about staying curious and adjusting as the tools evolve.

You do not need to be a developer to care about this

I know some of this sounds technical. Compaction, just in time retrieval, attention budgets. These are words that sound like they belong in a research paper.

But here is the truth. If you use AI at work, you are already doing context management, probably without realizing it. Every time you paste a document into Claude and then ask it a question, you are deciding what context to give it. Every time you start a new chat because the old one got confused, you are doing a rough form of compaction. Every time you tell an AI assistant to remember your writing style, you are doing memory management.

The difference between people who get amazing results from AI and people who get frustrated by it is rarely the tool. It is almost always the context. The better you get at thinking intentionally about what the model knows, what it is working with, and what it needs next, the better your outcomes will be.

Where to go from here

If you want to go deeper on this, the two best starting points are Anthropic's official post Effective Context Engineering for AI Agents and OpenAI's Agents SDK documentation on context management. Both are written in plain enough language that even non technical readers can get value from them.

And if you want a structured path to actually learn how to work with AI in a way that is practical and hands on, that is exactly why I built AI at Work Academy. Module 1 is free, no credit card, no commitment. It covers the foundational mindset and gives you your first real wins with AI tools in the context of your actual work.

Even if you do not take the course, do me a favor this week. Next time you open your favorite AI tool, pay attention to the context. What are you giving it? What are you leaving out? What could you give it that would make the answer ten times better? That single question, asked consistently, is what separates people who struggle with AI from people who thrive with it.