Learn how to engineer context for AI agents by managing tool feedback, optimizing token usage, and implementing strategies like offloading, compression, and isolation to build production-grade agents that perform reliably at scale.
Building AI agents that work reliably in production is fundamentally different from building simple chat applications. While chat models operate with a relatively static context window—primarily the user’s message and system instructions—agents face a far more complex challenge. Agents make tool calls in loops, and each tool’s output becomes part of the context that the LLM must process in the next step. This dynamic accumulation of context creates what many practitioners now call the “context engineering” problem. As more teams began building agents in 2024, a shared realization emerged: managing context is not a trivial task. It’s arguably the most critical engineering challenge when building production-grade agents. This article explores the principles, strategies, and practical techniques for context engineering that will help you build agents that scale efficiently, maintain performance, and keep costs under control.
Context engineering represents a fundamental shift in how we think about building AI systems. The term was popularized by Andrej Karpathy, who framed it as “the delicate art and science of filling the context window with just the right information for the next step.” This definition captures something essential: the context window of an LLM is like the RAM of a computer—it has limited capacity, and what you put into it directly affects performance. Just as an operating system carefully manages what data fits into a CPU’s RAM, engineers building agents must thoughtfully curate what information flows into the LLM’s context window at each step of execution.
The concept emerged from a shared experience across the AI engineering community. When developers first started building agents in earnest, they discovered that the naive approach—simply feeding all tool outputs back into the message history—led to catastrophic problems. A developer building a deep research agent, for example, might find that a single run consumed 500,000 tokens, costing $1 to $2 per execution. This wasn’t a limitation of the agent architecture itself; it was a failure to engineer the context properly. The problem isn’t just about hitting the context window limit, though that’s certainly a concern. Research from Chroma and others has documented what’s called “context rot”—a phenomenon where LLM performance actually degrades as context length increases, even when the model theoretically has capacity for more tokens. This means that blindly stuffing more information into the context window doesn’t just cost more money; it actively makes your agent perform worse.
Context engineering applies across three primary types of context that agents work with: instructions (system prompts, memories, few-shot examples, tool descriptions), knowledge (facts, historical information, domain expertise), and tools (feedback from tool calls and their results). Each of these requires different engineering approaches, and the challenge lies in orchestrating all three effectively as an agent executes over dozens or even hundreds of steps.
The importance of context engineering cannot be overstated for anyone building agents at scale. Consider the scale of modern agent systems: Anthropic’s multi-agent research system operates with agents that make hundreds of tool calls per task. Cognition’s research on agent architecture revealed that typical production agents engage in conversations spanning hundreds of turns. When you multiply the number of tool calls by the token cost of each tool’s output, you quickly understand why context management is the primary job of engineers building AI agents. Without proper context engineering, your agent becomes economically unviable and technically unreliable.
The economic argument is straightforward. If each agent run costs $1 to $2 due to excessive token consumption, and you’re running thousands of agents daily, you’re looking at thousands of dollars in daily costs that could be eliminated through better context management. But the performance argument is equally compelling. As context grows longer, LLMs experience multiple failure modes. Context poisoning occurs when a hallucination or error from an earlier step makes it into the context and influences all subsequent decisions. Context distraction happens when the sheer volume of information overwhelms the model’s ability to focus on the task at hand. Context confusion emerges when superfluous information influences responses in unexpected ways. Context clash occurs when different parts of the context contradict each other, creating ambiguity about what the agent should do next. These aren’t theoretical problems—they’re documented failure modes that teams encounter regularly when building agents without proper context engineering.
The stakes are particularly high for long-running agents. An agent that needs to research a complex topic, write code, debug it, and iterate might make 50 to 100 tool calls. Without context engineering, the message history would grow to include all intermediate results, all debugging output, all failed attempts. The agent would be trying to make decisions while drowning in irrelevant historical information. With proper context engineering, the agent maintains only the information it needs for the current step, dramatically improving both performance and cost efficiency.
A common source of confusion is the relationship between prompt engineering and context engineering. These terms are related but distinct, and understanding the difference is crucial for building effective agents. Prompt engineering, in its traditional sense, refers to the careful crafting of the initial prompt—the system message and user message—that you send to a language model. When you’re working with ChatGPT or Claude in a chat interface, you spend time optimizing that initial prompt to get better results. You might refine the instructions, add examples, clarify the desired output format. This is prompt engineering, and it remains important.
Context engineering is a broader concept that encompasses prompt engineering but extends far beyond it. Context engineering applies specifically to agents, where the context isn’t static—it’s dynamic and evolving. With a chat model, the human message is the primary input, and most of the engineering effort goes into crafting that message. With an agent, the game is fundamentally different. The agent receives context not just from the human’s initial request but from tool calls that execute during the agent’s trajectory. At each step of the agent’s execution, new context flows in from the tool’s output. This creates a cascading problem: if you naively include all of that tool output in the message history, your context window grows exponentially with each step.
Think of it this way: prompt engineering is about optimizing the initial conditions. Context engineering is about managing the entire flow of information throughout the agent’s lifecycle. It includes decisions about what tool outputs to include, how to summarize them, when to compress the message history, whether to offload information to external storage, and how to structure the agent’s state to minimize irrelevant context. Prompt engineering is a subset of context engineering. The system instructions and user instructions are still important—they’re part of the context that needs to be engineered. But context engineering also encompasses all the strategies for managing the dynamic context that accumulates as the agent executes.
The most practical framework for context engineering breaks down into four complementary strategies: write, select, compress, and isolate. These strategies can be implemented individually or combined, and they form the foundation of how production agents manage context effectively. Understanding each strategy and knowing when to apply it is essential for building agents that scale.
The “write” strategy involves saving context outside the context window so that it’s available to the agent but doesn’t consume tokens in the message history. This is perhaps the most powerful context engineering technique because it directly addresses the token accumulation problem. Rather than including all tool outputs in the message history, you write them to an external system and keep only a reference or summary in the context.
Scratchpads are one implementation of this strategy. The concept is borrowed from how humans solve complex problems—we take notes, jot down intermediate results, and refer back to them as needed. Agents can do the same. Anthropic’s multi-agent research system provides a clear example: the LeadResearcher agent saves its plan to memory at the beginning of the task. This is crucial because if the context window exceeds 200,000 tokens, it will be truncated, and losing the plan would be catastrophic. By writing the plan to a scratchpad, the agent ensures that this critical information persists even if the context window fills up. Scratchpads can be implemented in several ways: as a tool call that writes to a file system, as a field in the agent’s runtime state object (as in LangGraph), or as entries in a database. The key is that the information is stored externally and can be retrieved on demand.
Memories extend this concept across multiple sessions or threads. While scratchpads help an agent solve a single task, memories help agents learn and improve across many tasks. The Reflexion framework introduced the idea of reflection—after each agent turn, the agent generates a summary of what it learned and stores it as a memory. Generative Agents took this further, synthesizing memories periodically from collections of past feedback. These concepts have made their way into popular products like ChatGPT, Cursor, and Windsurf, which all auto-generate long-term memories that persist across sessions. An agent can store episodic memories (examples of desired behavior), procedural memories (instructions for how to do things), and semantic memories (facts and domain knowledge). By writing these memories externally, the agent can maintain a rich knowledge base without bloating the context window.
The challenge with the write strategy is determining what to write and how to organize it. You don’t want to write everything—that defeats the purpose. You want to write information that’s useful for future steps but not immediately needed. For a deep research agent, you might write full articles to disk and keep only a summary in the context. For a code agent, you might write the full codebase to a file system and keep only the current file being edited in the context. The key is being selective about what gets written and ensuring that what remains in the context is sufficient for the agent to know what’s been written and how to retrieve it if needed.
The “select” strategy is about choosing which context to include in the message history at each step. This is where the agent decides what information it actually needs for the current decision. If you’ve written context to external storage, you need a mechanism for selecting what to pull back in when it’s relevant. This can be as simple as the agent making a tool call to read a file, or it can be more sophisticated, using embeddings or knowledge graphs to find semantically relevant information.
For scratchpads, selection is often straightforward. The agent can read the scratchpad whenever it needs to reference the plan or previous notes. For memories, selection is more complex. If an agent has accumulated hundreds of memories across many sessions, it can’t include all of them in the context. Instead, it needs to select the most relevant ones. This is where embeddings become useful. You can embed each memory and use semantic search to find the memories most relevant to the current task. ChatGPT’s memory system is a good example of this in practice—it stores user-specific memories and selects relevant ones to include in the context based on the current conversation.
The challenge with selection is ensuring that you select the right information. If you select too little, the agent lacks important context and makes poor decisions. If you select too much, you’re back to the original problem of bloated context. Some agents use a simple heuristic: always include certain files or memories (like a CLAUDE.md file in Claude Code, or a rules file in Cursor). Others use more sophisticated selection mechanisms based on semantic similarity or explicit agent reasoning about what’s relevant. The best approach depends on your specific use case, but the principle is clear: be intentional about what context you include at each step.
The “compress” strategy involves reducing the size of context while retaining the information the agent needs. This is different from simply deleting context—compression means summarizing, abstracting, or reformatting information to make it more concise. Compression is particularly important for managing message history as an agent executes over many steps. Even with offloading and selection, the message history can grow significantly. Compression helps keep it manageable.
One approach to compression is summarization. When an agent completes a phase of work, you can summarize what happened and replace the detailed logs with the summary. For example, if an agent spent 10 steps researching a topic and made 10 tool calls, you could replace all of that with a single summary: “Researched topic X and found that Y is the key insight.” This preserves the essential information while dramatically reducing token count. The challenge is doing this summarization in a way that preserves recall—the agent needs to know enough about what was summarized to decide whether it needs to retrieve the full details.
Cognition’s research on agent architecture emphasizes that summarization deserves significant engineering effort. They even use fine-tuned models specifically for summarization to ensure that all relevant information is captured. The key is prompt engineering the summarization step carefully. You want to instruct the summarization model to capture an exhaustive set of bullet points about what’s in the original context, ensuring that the agent can later decide whether to retrieve the full details. This is different from casual summarization—it’s compression with high recall.
Another compression technique is agent boundaries. In multi-agent systems, you can compress context at the boundaries between agents. When one agent hands off work to another, you don’t pass the entire message history. Instead, you pass a compressed summary of what was accomplished and what the next agent needs to know. This is where the distinction between single-agent and multi-agent systems becomes important. While multi-agent systems introduce complexity in communication, they also provide natural points for compression and context isolation.
The “isolate” strategy involves using multiple agents with separate contexts rather than a single agent with a monolithic context. This is the multi-agent approach, and it’s particularly useful for complex tasks that naturally decompose into subtasks. By isolating context to specific agents, you prevent context from growing unboundedly and you allow each agent to focus on its specific role.
The argument for multi-agent systems is compelling from a context engineering perspective. If you have a single agent handling research, writing, and editing, its context window will include information about all three tasks. But when the agent is writing, it doesn’t need the research details in the context—it just needs the key findings. When it’s editing, it doesn’t need the research details either. By using separate agents for research, writing, and editing, each agent’s context can be optimized for its specific task. The research agent includes research tools and research context. The writing agent includes writing tools and the research findings. The editing agent includes editing tools and the draft to edit. Each agent’s context is smaller and more focused.
The challenge with multi-agent systems is communication. When one agent hands off work to another, you need to ensure that sufficient context is communicated. This is where the compression strategy becomes critical. The research agent needs to compress its findings into a form that the writing agent can use. The writing agent needs to compress the draft in a way that the editing agent can work with. Cognition’s research argues that this communication overhead can be significant and that careful engineering is required to make multi-agent systems work well. However, when done right, multi-agent systems can dramatically reduce context bloat and improve overall system performance.
FlowHunt’s workflow automation capabilities are particularly well-suited to implementing multi-agent systems with proper context isolation. By defining clear workflows with distinct agents and explicit handoff points, you can ensure that context is managed efficiently at each stage. FlowHunt allows you to define the state that flows between agents, implement compression at handoff points, and monitor context usage across your agent system.
Understanding the four strategies is one thing; implementing them effectively is another. Let’s walk through a concrete example: building a deep research agent. A naive implementation would have the agent make a series of web searches, include all the search results in the message history, and let the agent synthesize them. This quickly becomes expensive and ineffective. A well-engineered implementation would use all four strategies.
First, the agent would use the “write” strategy to save full articles to disk as it retrieves them. Rather than including the full text in the message history, it would keep only a reference or a summary. Second, it would use the “select” strategy to pull in only the most relevant articles when synthesizing findings. Third, it would use the “compress” strategy to summarize its research findings into key bullet points before moving to the next phase. Fourth, if the task is complex enough, it might use the “isolate” strategy by having separate agents for research, synthesis, and writing, each with its own optimized context.
The implementation details matter. For the write strategy, you need to decide where to store the articles—a file system, a database, or a vector store. For the select strategy, you need to decide how to retrieve relevant articles—keyword search, semantic search, or explicit agent reasoning. For the compress strategy, you need to carefully prompt the summarization step to ensure high recall. For the isolate strategy, you need to define clear agent boundaries and communication protocols.
One critical insight from production experience is that context engineering is not a one-time optimization—it’s an ongoing process. As your agent executes, you should monitor context usage, identify bottlenecks, and iteratively improve your context engineering. Tools like LangGraph provide visibility into agent state and context flow, making it easier to identify where context is accumulating unnecessarily. FlowHunt extends this by providing workflow-level visibility, allowing you to see how context flows through your entire agent system and identify optimization opportunities.
Building context-engineered agents in production reveals challenges that aren’t obvious from theory. One common challenge is the “context selection problem”—how do you know what context is actually relevant? An agent might have access to hundreds of documents, thousands of memories, or vast amounts of historical data. Selecting the right subset is non-trivial. Semantic search using embeddings helps, but it’s not perfect. Sometimes the most relevant information is something the agent wouldn’t think to search for. Some teams address this by having agents explicitly reason about what context they need, making tool calls to retrieve specific information rather than relying on automatic selection. Others use a combination of semantic search and explicit agent reasoning.
Another challenge is the “summarization quality problem”—how do you summarize context without losing critical information? A poorly summarized context can mislead the agent into making wrong decisions. The solution is to invest in the summarization step. Carefully prompt the summarization model. Test different summarization approaches. Consider using a fine-tuned model if you have enough data. Monitor whether the agent is making decisions that suggest it’s missing important information from the summarized context.
A third challenge is the “multi-agent communication problem”—how do you ensure that context is communicated effectively between agents? This is where explicit protocols matter. Define exactly what information each agent should pass to the next. Use structured formats (JSON, for example) rather than free-form text. Include metadata about what’s in the context so the receiving agent knows what it’s working with. Test the communication protocol with realistic scenarios to ensure it works in practice.
Effective context engineering requires measurement. You need to understand how much context your agent is using, where it’s accumulating, and how it’s affecting performance. Key metrics include total tokens per run, tokens per step, context window utilization, and performance metrics like task success rate and latency. By tracking these metrics, you can identify when context engineering is working and when it needs improvement.
Token usage is the most obvious metric. Track how many tokens your agent uses per run and per step. If token usage is growing over time, that’s a sign that context is accumulating. If token usage is high relative to the task complexity, that’s a sign that context engineering could be improved. Cost is another important metric—if your agent is expensive to run, context engineering is likely the culprit.
Performance metrics are equally important. Track whether your agent is making better or worse decisions as context grows. If performance degrades with longer context, that’s evidence of context rot. If performance improves with better context engineering, that validates your approach. Success rate, latency, and error rate are all useful metrics to track.
FlowHunt’s analytics capabilities make it easier to monitor these metrics across your agent workflows. By integrating context engineering monitoring into your workflow platform, you can see at a glance how well your context engineering is working and identify opportunities for improvement.
As agent technology matures, more sophisticated patterns are emerging. Ambient agents, for example, are agents that run continuously in the background, maintaining state and context across many interactions. These agents face unique context engineering challenges because they need to maintain relevant context over long periods while avoiding context bloat. The solution involves sophisticated memory management, periodic compression, and careful context isolation.
Another emerging pattern is continuous context management—rather than engineering context once at the beginning of an agent’s execution, you continuously refine and optimize context as the agent runs. This might involve periodically compressing the message history, removing irrelevant context, or reorganizing context for better performance. This requires more sophisticated agent architectures and better tooling, but it can dramatically improve performance for long-running agents.
These advanced patterns are still being explored and refined, but they represent the future of agent engineering. As agents become more capable and are deployed in more complex scenarios, context engineering will become increasingly sophisticated.
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Context engineering is still a relatively new discipline, but it’s rapidly becoming a core competency for AI engineers. As LLMs become more capable and agents become more complex, the importance of context engineering will only grow. We’re likely to see more sophisticated tools and frameworks specifically designed for context engineering. We’ll see more research into optimal context management strategies. We’ll see best practices emerge and solidify.
One promising direction is the development of better abstractions for context management. Rather than manually implementing context engineering strategies, developers might use frameworks that handle context engineering automatically. LangGraph is moving in this direction by providing better primitives for managing agent state and context flow. FlowHunt is extending this by providing workflow-level abstractions that make it easier to implement context engineering patterns across complex agent systems.
Another promising direction is the development of better metrics and monitoring for context engineering. As we get better at measuring context usage and its impact on performance, we’ll be able to optimize more effectively. Machine learning techniques might even be applied to automatically optimize context engineering strategies based on observed performance.
The field is moving quickly, and best practices are still evolving. But the core principles are clear: context is a precious resource, it needs to be engineered carefully, and the effort invested in context engineering pays dividends in performance, reliability, and cost efficiency.
Context engineering is the art and science of managing information flow through AI agents to optimize performance, reliability, and cost. By understanding and implementing the four core strategies—write, select, compress, and isolate—you can build agents that scale effectively and maintain performance even as they execute over dozens or hundreds of steps. The key is recognizing that context management is not an afterthought or a minor optimization; it’s the primary engineering challenge when building production-grade agents. Start by measuring your current context usage, identify where context is accumulating unnecessarily, and apply the appropriate strategies to optimize. Monitor the results and iterate. With careful context engineering, you can build agents that are both powerful and efficient.
Context engineering is the art and science of filling an LLM's context window with just the right information at each step of an agent's trajectory. It involves managing instructions, knowledge, and tool feedback to optimize agent performance while minimizing token costs and performance degradation.
Prompt engineering focuses on crafting the initial system and user messages for chat models. Context engineering is broader and applies specifically to agents, where context flows in dynamically from tool calls during the agent's execution. It encompasses managing all context sources throughout the agent's lifecycle, not just the initial prompt.
The four primary strategies are: Write (saving context externally via scratchpads and memories), Select (pulling relevant context into the window), Compress (reducing context size while maintaining information), and Isolate (separating context across multiple agents to prevent interference and manage complexity).
Agents make multiple tool calls in sequence, and each tool's output is fed back into the LLM's context window. Without proper context management, this accumulation of tool feedback can quickly exceed the context window, increase costs dramatically, and degrade performance through context rot and other failure modes.
FlowHunt provides workflow automation tools that help manage agent execution, context flow, and state management. It enables you to implement context engineering strategies like offloading, compression, and isolation within your agent workflows, reducing token costs and improving reliability.
Arshia is an AI Workflow Engineer at FlowHunt. With a background in computer science and a passion for AI, he specializes in creating efficient workflows that integrate AI tools into everyday tasks, enhancing productivity and creativity.
Build smarter AI agents with intelligent context engineering. FlowHunt helps you manage agent workflows, optimize token usage, and scale production agents efficiently.
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