Handling Limited Context Windows in AI Agents: 7 Practical Strategies

Anyone who has built an agent that runs more than a handful of steps knows the moment. An error says the context window was exceeded, or the model suddenly acts as if the conversation from twenty messages ago never happened. Tool calls, long chains of reasoning and bulky JSON responses all eat tokens at an alarming rate.

The problem grows with the agent's complexity. The longer it works, the more state it has to carry, and sooner or later the window becomes the bottleneck. Seven techniques help here. You won't need all of them, since two or three often change cost, speed and reliability noticeably. The aim is to get more done inside the same window.

1. Compress the history

This is the most common technique in real systems. Rather than replaying the whole conversation, you keep a running summary of what matters:

• decisions that were made
• reasoning worth keeping
• final results from tools
• user preferences and current state

Older turns get squeezed down, and only the latest ones stay in full detail. Drop the examples and small talk, and hold on to the decisions and outcomes.

After twenty messages, for instance, the whole backlog could shrink to a note such as: the user wants a Python command-line app, Click was chosen, three commands exist, tests and docs are still open. The model doesn't need every earlier message. It needs whatever affects its next move.

Compression can also be layered: a summary per step, another per task, and a global project memory on top.

2. Prune messages and filter for relevance

Not every message deserves to stay forever. As an agent works, its context fills with material that has stopped being useful. You can:

• discard old tool logs once their results have been absorbed
• delete huge JSON payloads after pulling out the key fields
• keep final answers and drop the intermediate chatter

Picture a web search that returns a 20,000-token HTML page. Keeping the raw page in context makes no sense. Parse it once, extract what's needed, and throw the markup away. The model still gets the useful facts without hauling thousands of pointless tokens around.

3. Hand heavy computation to tools

Math, simulations and data transformations shouldn't be reasoned through token by token. A Python tool or backend service can do that work, and the model only receives a compact outcome, such as one number, a small structured summary or a few key statistics.

Suppose a huge CSV needs analysis. Don't paste it into the prompt. Let a tool compute the statistics and report something like "100k rows, average price 23.4, 5% nulls in column X". That's far cheaper than shipping the full dataset into the window. The model reasons, and the tools crunch.

4. Keep stable rules in the system prompt

Some instructions never change during a conversation:

• the assistant's tone
• the tools that exist
• formatting requirements
• reference examples

State them once in the system message and stop repeating them. If an agent always has to answer in one specific JSON shape, there's no reason to restate that in every user turn. Depending on the instruction size and how often the model gets called, the savings over a long session can reach hundreds or even thousands of tokens.

5. Pass handles, not blobs

Imagine a database schema made of thousands of lines of SQL, roughly 20,000 tokens. Instead of placing it in the window, tell the agent that the schema lives under a name like schema_v2 and can be fetched through a get_schema tool when needed.

The schema now sits in a tool or a separate resource, and the agent loads it only when it truly has to. This approach suits any large resource:

• database schemas
• long documents
• configuration files
• API documentation
• knowledge bases

The agent holds a small reference and pulls the real content on demand.

6. Shrink tool output before the model sees it

Raw, enormous tool output shouldn't go straight into the model if it can be avoided. Some typical reductions:

• a big dataset becomes row counts plus the relevant columns
• a full web page becomes its title, headings and metadata
• a huge JSON document becomes only the keys and fields that matter for the decision

Say a database tool finds thousands of rows, but the agent only needs the row count and an average for one column. Sending every row back would be wasteful. The tool can answer with a few lines instead:

100,000 rows
Average price: 23.4
Null values in column X: 5%

Now the model can reason about the result with a tiny token budget. Tools work best as pre-processors, turning masses of raw data into the small slice the model actually needs, and not as plain data pipes.

7. Use a sliding window

The last technique keeps just the most recent N messages, for example the last ten, together with a summary of earlier turns and a small store of important facts. Each request carries a limited recent window plus the essentials from the past, rather than the full transcript.

The recent part might look like this:

Recent context:
Message 31
Message 32
...
Message 40

and the part carrying older information like this:

Earlier summary:
User is building a Python CLI app.
Click was selected.
Three commands have been implemented.Important memory:
Tests and documentation are still pending.

It's a simple approach that holds up well and fits a huge range of use cases. Plenty of chatbots use some variant of it because it balances context, cost and quality in a practical way.

Choosing where to begin

To recap, the seven techniques are:

1. context compression
2. message pruning and relevance filtering
3. offloading computation to tools
4. system prompts for stable rules
5. placeholder handles instead of full text
6. condensed tool output
7. a sliding window

Compression, pruning and condensed tool output form a strong starting trio, and they usually produce big gains on their own. The deeper lesson is that an agent doesn't have to drag everything along. It has to carry the right information. Once context is treated as a scarce resource instead of unlimited memory, designing agents that scale gets much easier.