How to Engineer Context for Scalable AI Agents

Discover effective context engineering techniques like offloading, reducing, and isolating to enhance AI agent scalability and performance.

Chris

Dec 17, 2025 — 5 min read

Artificial Intelligence (AI) agents are transforming the way businesses operate by automating complex tasks and improving efficiency. However, as the scope of tasks AI agents handle continues to grow, so do the challenges associated with managing their performance and scalability. One critical issue in this space is context management, particularly as it relates to large language model (LLM)-based agents.

In this article, we’ll explore how to engineer context effectively for scalable AI agents, focusing on principles like offloading, reducing, and isolating context. We'll also dive into the common practices employed by popular AI solutions like Cloud Code, Manis, and the Deep Agents package, providing actionable insights for business leaders and AI enthusiasts alike.

Why Context Engineering Matters in Scalable AI Agents

At their core, AI agents operate in a loop: an LLM makes a tool call, the tool performs its action and returns an observation, and the agent uses that information to decide its next move. This loop becomes increasingly complex as AI agents take on longer tasks.

Research shows that the average task length is doubling every seven months, resulting in agents performing tens or even hundreds of tool calls within a single task. The challenge lies in how these agents manage the accumulation of data in their context windows - the temporary memory space used to process information. Mismanagement of context leads to degraded agent performance, increased latency, and inflated operational costs.

This growing complexity necessitates a structured approach to context engineering, the science of ensuring agents have access to "just the right information" for the next step in their workflow.

Core Challenges in Context Management

Performance Degradation: Studies, including Chrome’s report on context rot, show that as context windows become saturated, the performance of AI agents decreases.
High Cost and Latency: Reprocessing large amounts of context in every turn significantly increases the operational burden.
Scalability Issues: Without intelligent context management, scaling agents to handle longer, more complex tasks becomes unsustainable.

To address these challenges, three key principles have emerged: offloading, reducing, and isolating context. Let’s break down each principle in detail.

1. Offloading Context: Using External Storage as a Memory Extension

Offloading context involves moving data out of the LLM's limited context window to an external storage system, such as a file system, where it can be selectively retrieved when needed. This approach ensures that essential information persists without overloading the agent’s active memory.

How Offloading Works

File Systems as Memory: AI agents can save intermediate results, plans, or task logs to a file system during long-running operations. This allows the agent to reference these files later without retaining their content in the context window.
Persistent Memory: Tools like Cloud Code and Deep Agents employ persistent storage solutions, such as cloud.md files or memory directories, enabling agents to maintain memory across different invocations.

Real-World Applications

Plan Reinforcement: For instance, an agent tasked with executing a complex series of subtasks can write its plan to a file and retrieve it whenever necessary. This prevents the agent from deviating from its intended workflow.
Simplified Tooling: By offloading certain tasks to scripts in the file system, agents can use fewer tools while still accessing extensive functionality. For example, Manis employs lightweight tools like Bash and file manipulation capabilities, allowing it to execute predefined scripts without overloading the system prompt with tool descriptions.

2. Reducing Context: Streamlining Data to Optimize Efficiency

Reducing context ensures that only the most relevant and necessary information is retained in the agent’s active memory. This is achieved through techniques like compaction, summarization, and filtering.

Key Techniques for Context Reduction

Compaction

Description: Old tool results or stale data are saved to a file system, and their full content is replaced with file references in the message history.
Example: Manis utilizes compaction to manage the saturation of its context window, reducing token usage without losing access to detailed historical data.

Summarization

Description: Summarization algorithms distill large volumes of tool results and conversation history into concise summaries that maintain the essence of the original content.
Example: The Deep Agents package applies summarization middleware once the context reaches a threshold of 170,000 tokens, ensuring efficient operation even during long tasks.

Filtering

Description: Large or irrelevant tool results are filtered out to prevent them from unnecessarily consuming memory.
Example: The Deep Agent CLI filters excessively large tool results to ensure that only actionable data is passed through the context window.

Practical Considerations

While summarization can save tokens, it is irreversible and requires careful implementation to avoid losing critical information. In contrast, compaction is reversible, as the raw data is stored in external files, making it a safer option for high-stakes operations.

3. Isolating Context: Using Sub-Agents for Task Segmentation

Context isolation involves assigning specific tasks to sub-agents with their own independent context windows. This approach prevents tasks from interfering with one another and ensures that each sub-agent operates in a clean, focused environment.

How It Works

Parent-Child Architecture: A main agent delegates specific tasks to sub-agents. These sub-agents execute their tasks independently and return results to the parent agent.
Shared Context Options: While sub-agents generally operate with isolated context, they can share some context with the parent agent, such as stored files or specific instructions.

Benefits of Context Isolation

Scalability: Isolated tasks are easier to manage and scale, as each sub-agent operates within its own memory constraints.
Efficiency: By breaking tasks into self-contained units, sub-agents can execute them faster without the overhead of unrelated context data.

Use Cases

Manis, Deep Agents, and Cloud Code all leverage sub-agent architectures to process complex workflows more efficiently. For example, the Deep Agent CLI allows sub-agents to access the same file system as the parent agent, enabling seamless collaboration without memory bloat.

Key Takeaways

Offloading Context: Use external file systems to store task-related data and maintain memory across agent invocations. This approach reduces context window saturation and improves scalability.
Reducing Context: Employ strategies like compaction, summarization, and filtering to optimize token usage without sacrificing essential information.
Isolating Context: Utilize sub-agents with independent context windows to handle specific tasks, ensuring clean and scalable workflows.
Token Efficiency: Minimize the number of tools an agent uses by employing lightweight, general-purpose tools like Bash, combined with external scripts that expand functionality.
Progressive Disclosure: Avoid overwhelming the system prompt by loading only essential tool instructions initially and retrieving detailed descriptions on-demand.

Conclusion

Effective context engineering is essential for building scalable AI agents capable of handling increasingly complex tasks. By offloading, reducing, and isolating context, organizations can improve agent performance, reduce operational costs, and unlock new possibilities for AI-driven automation.

Whether you’re a business leader exploring AI for operational efficiency or an AI developer looking to stay at the forefront of emerging technologies, understanding these principles will empower you to design smarter, more scalable AI systems. As the field of AI continues to evolve, mastering context engineering will be crucial for leveraging the full potential of intelligent agents.

Source: "How Agents Use Context Engineering" - LangChain, YouTube, Nov 12, 2025 - https://www.youtube.com/watch?v=XFCkrYHHfpQ