Taming the Context Window: Strategies for Intelligent AI Workflows

When building AI-powered workflows, we often face the challenge of managing context windows. This is especially true when our AI agents perform complex tasks by chaining multiple tool calls, where the output of one tool becomes the input for the next. The problem arises when we exceed the Large Language Model (LLM) provider’s context window, which can lead to errors and inefficient processing.

I’ve been exploring ways to implement intelligent context windows that can maintain previous tool call information while keeping context windows manageable. Here are some strategies I’ve considered:

## Summarization

Condensing tool outputs before storing them in memory can help reduce the context window size. This approach involves identifying the most critical information from each tool’s output and summarizing it in a concise manner.

## Selective Retention

By keeping only the relevant fields or information for downstream steps, we can significantly reduce the amount of data stored in memory. This approach requires a deep understanding of the workflow and the dependencies between tools.

## External Storage

Offloading large outputs to a database or object storage can help alleviate the context window issue. By storing references to these outputs in memory, we can quickly retrieve the required information when needed.

## Memory Pruning

Implementing a sliding window or relevance-based trimming of memory can help remove unnecessary data and prevent the context window from growing excessively.

## Hierarchical Memory

Using a multi-level memory structure, where detailed information is summarized at higher levels, can provide a more efficient way of storing and retrieving data.

These strategies can help us build more efficient and scalable AI workflows. But I’d love to hear from the community – have you dealt with similar challenges? What approaches have you found effective in maintaining workflows without hitting context limits? Share your experiences and best practices for structuring memory in AI agent systems.

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*Further reading: [Context Window in AI Workflows](https://www.ibm.com/topics/context-window)*