The Next Step in Agentic AI: Context Engineering?

Author(s): Burak Degirmencioglu

Originally published on Towards AI.

Our interactions with artificial intelligence have become much more complex and dynamic than simply giving commands. We now expect them not only to answer our questions but also to complete multi-step tasks, use tools, and remember past interactions. This new expectation has pushed the boundaries of the once-popular technique of “Prompt Engineering” and led us toward a more holistic approach: “Context Engineering.” In this article, we will delve into what this advanced engineering technique is, why it has become so critical, its core components, and the most effective application techniques used to maximize the performance and reliability of AI agents.

What Exactly is Context Engineering?

Context Engineering is the discipline of designing, structuring, and optimizing the entire informational environment required for an AI model, especially for autonomous agents, to successfully complete their tasks. This involves much more than just sending a single prompt; it is about creating a holistic “workspace” that includes the model’s identity, goals, available tools, accessible data, and past interactions. The main goal is to guide the model’s reasoning process to ensure it exhibits more consistent, accurate, and goal-oriented behavior.

To better understand why this holistic approach is necessary, we must question why “Prompt Engineering,” the hallmark of a previous era in AI interactions, has become insufficient for today’s complex tasks.

Going Beyond Simple Commands: Why is Prompt Engineering No Longer Enough on Its Own?

Prompt Engineering was an art focused on crafting the most effective command or question to get a desired output from a language model. However, as AI agents began to undertake complex tasks involving multiple steps (e.g., creating a travel plan and making reservations) instead of generating one-off responses, this approach became inadequate. This is because such tasks require state tracking, memory, the use of external tools, and adaptation to dynamically changing conditions. While Prompt Engineering optimizes a model’s performance at a single moment of interaction, Context Engineering provides a persistent and structured environment that allows the model to act consistently and intelligently throughout the entire duration of a task. Context-Engineering-Guide, Cognition.ai – Context Engineering

The inadequacy of Prompt Engineering has pushed us toward a more comprehensive solution. So, where exactly are the key lines that separate these two engineering techniques?

More Than Just Giving a Command: What is the Fundamental Difference Between Context and Prompt Engineering?

The difference between Prompt Engineering and Context Engineering is similar to the difference between asking someone for directions and giving an architect a complete project file to build a building. Prompt Engineering aims to get a better answer by refining the question posed to the model and is typically limited to a single interaction. Context Engineering, on the other hand, builds the entire world or informational environment in which the model will operate. This includes the model’s role, rules, capabilities, accessible information, and past interactions, allowing the model to solve a complex problem. In short, the former aims for instant output, while the latter aims for long-term and consistent performance. Phil Schmid — Context Engineering

Once we understand this fundamental distinction, we can examine the core building blocks that make up this effective “workspace” for an AI.

How to Build an Effective AI Workspace?

An effective context consists of various components that are assembled to help the AI successfully carry out its task. These components help the model understand both what it needs to do and how to decide how to do it.

• System Prompt: These are the main instructions that define the model’s core identity, purpose, behavioral rules, and constraints. It acts like the agent’s “constitution,” outlining the fundamental framework it must follow throughout all its operations. For example, for a customer service agent, the system prompt might include basic rules like, “You are a kind, helpful assistant who only provides information about the company’s products.”
• Instructions: These are specific instructions for the task to be performed at that moment. While the system prompt provides the general framework, instructions specify immediate goals, such as “find the user’s last order status and inform them via email.”
• Structured Inputs and Outputs: This involves providing inputs and outputs in a standard format, such as JSON or XML, to reduce ambiguity and process information more consistently. This approach helps the model understand the data correctly and ensures that the generated response can be easily processed by other systems. For example, instead of saying “Find Ahmet’s information,” providing a structured input like {"command": "get_user_info", "name": "Ahmet"} helps the model understand your intent precisely.
• Tools: These are external functions or APIs that extend the model’s capabilities beyond its basic language processing capacity. They can include functions like searching the web, querying a database, running code, or using a calculator. The model decides which of these tools to use and when to use them to complete its task.
• RAG & Memory: Retrieval-Augmented Generation (RAG) allows the model to instantly pull and use the most relevant information from external knowledge sources (e.g., a company’s internal documentation database) that are too large or constantly updated to fit within the context window. Memory allows the model to remember past conversations or interactions, enabling it to provide more consistent and personalized responses.
• States & Historical Context: This is a record that tracks the current state of the task or conversation and the steps taken so far. This allows the model to answer the questions “where are we now?” and “how did we get here?” — especially in multi-step processes — enabling it to take the next logical step. Langchain — Context Engineering for Agents

After building a strong foundation by combining these components, we need to employ specific techniques to actively manage and optimize this context during the task.

What Are the Most Effective Context Engineering Techniques?

Context Engineering involves not only bringing the right components together but also dynamically managing that information. This management is shaped around four main techniques:

• Write Context: This is the foundational step of the process, where all the information the agent will use (system prompt, instructions, tool definitions, etc.) is initially created and formatted. All the starting “capital” the agent has before beginning its task is provided at this stage.
• Select Context: At a specific moment in a task, this involves selecting only the most relevant information from all available data and presenting it to the model. For example, when a software development agent is given a debugging task, adding only the files and documents related to the module where the error occurred — instead of all project files — helps the model maintain focus.
• Compress Context: This is the process of converting a large amount of information (like a long conversation history or a voluminous document) into a shorter, more condensed form without losing its core meaning. Techniques such as summarization or embedding are used to fit more meaningful information into a limited context window.
• Isolate Context: This is a technique to prevent information leakage by creating separate, “sandbox-like” contexts for independent subtasks. This prevents the complex or “dirty” data of one subtask from negatively affecting the reasoning process of other subtasks. It’s like providing the model with a clean workspace for each new problem. Langchain — Context Engineering for Agents

These techniques are vital for overcoming the challenges that AI models face, especially when working with what are known as “long contexts” or large information windows. However, these large windows also carry their own serious risks.

Drowning in an Ocean of Information: What Are the Risks of Long Context Windows?

Even though modern language models have increasingly larger context windows, filling that window with irrelevant or contradictory information can lead to serious problems. These issues can significantly reduce a model’s performance and reliability.

• Context Poisoning: This is the intentional or unintentional addition of false, misleading, or irrelevant information to the context. This “toxic” information can corrupt the model’s reasoning process, causing it to produce erroneous or undesirable outputs. For example, adding an incorrect configuration detail among technical documents about a product can lead the model to provide completely wrong installation instructions.
• Context Distraction: This is when the model gets hung up on an insignificant detail in the context that is unrelated to the main task. Similar to how humans might get sidetracked by a trivial detail and lose focus on the main point, the model can focus on a “noisy” piece of information in the context and lose sight of its primary goal.
• Context Confusion: This occurs when there are multiple conflicting or ambiguous pieces of information in the context. When the model cannot distinguish which instruction or piece of information is correct, it becomes indecisive or takes a wrong action. A situation where the context contains instructions like both “use method A” and “never use method A” is an example of this.
• Context Clash: This is a conflict between the rules stated in the model’s core system prompt and information added later through user instructions or documents retrieved with RAG. This situation creates a conflict between the model’s predefined identity and its immediate task, leading to unpredictable behavior. dbreunig — How Long Context Fails

Fortunately, these types of context problems are not inevitable. There are powerful strategies we can use to fix a messy or corrupted context and restore the AI’s performance to the desired level.

How to Fix a Messy Context and Improve AI Performance?

Repairing a failing or at-risk context directly impacts an agent’s performance and reliability. Here are the most effective strategies used for this purpose:

• RAG (Retrieval-Augmented Generation): Instead of loading all possible information into the context from the start, RAG is a “just-in-time” information delivery system that fetches only the most relevant pieces from a large knowledge base when needed. This keeps the context clean and minimizes unnecessary information that could distract the model.
• Tool Loadout: Instead of presenting the model with all available tools, this involves providing only a specific set of tools necessary to complete the task at hand. For example, for a data analysis task, the model is given only a Python interpreter and database query tools, while the email sending tool is kept out of context.
• Context Quarantine: This technique isolates potentially “dirty” or misleading information — especially from user inputs or untrustworthy documents — from the main reasoning process. The model can be directed to handle this “quarantined” information more carefully or to check it with a special tool (e.g., a validation function) before processing it.
• Context Pruning: As a task progresses, this involves actively removing information from the context window that has become irrelevant or is no longer valid. Much like a gardener pruning dry branches, this keeps the workspace clean and focused.
• Context Summarization: As a conversation history or task steps become long, this involves periodically summarizing past interactions to retain the main ideas and make room in the context window. This allows the model to focus on new information without forgetting important details from the past.
• Context Offloading: This is the practice of storing large documents or datasets outside of the context window, in an external tool or database. Instead of giving the information directly to the model, it is provided with a reference or a function it can access when needed, preventing the main context from becoming unnecessarily bloated. dbreunig — How to Fix Your Content

In conclusion, as the capabilities of AI systems increase, our interactions with them must also become more sophisticated. The shift from Prompt Engineering to Context Engineering is not just a technical change but also a mindset revolution — from using AI as a tool to designing a productive and reliable workspace for it. A successful Context Engineer is an information architect who ensures the model not only provides the right answers but also follows the correct reasoning processes. By adopting these principles and techniques, we can develop smarter, more reliable, and more capable AI agents.

I’d love to hear your thoughts and experiences about this exciting field. Share your own “Context Engineering” adventures in the comments, and don’t forget to like this article if you found it helpful!

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Published via Towards AI