Context engineering is the future of building AI agents

Context is the bedrock on which meaningful interactions are built.

---

We’re at the brink of a major shift in AI. What began as simple, task-specific models is now evolving into something far more powerful: multi-turn, reasoning-driven agents that can plan, act and adapt. In this new era, context isn’t just useful – it’s the bedrock on which meaningful interactions are built.

Enter context engineering – the emerging discipline of designing and delivering the right information at the right time to AI systems. When done right, it allows agents to reason more intelligently, respond more precisely and deliver experiences that feel informed, human and relevant.

Tools like OpenAI’s function calling, long-term memory, tool use and agentic workflows have made context engineering not just an advantage, but a necessity.

At its core, context engineering is about enabling AI agents to “think” more like humans – with memory, continuity and relevance. This means identifying what information is needed for a specific task or goal, structuring it intelligently (whether that’s user preferences, documents, past conversations or intent) and then feeding it into the AI system in a way it can process and use.

This might be done through carefully crafted prompts, by retrieving information from a knowledge base (often referred to as RAG – retrieval-augmented generation), or by integrating memory and tool usage. Each of these methods plays a role in helping the agent maintain consistency and depth over time.

So what goes into context engineering? First, there's prompt design, which remains a powerful mechanism for steering responses, but is only the beginning. Retrieval-augmented generation lets agents dynamically fetch relevant information at the moment it’s needed, from internal documents to external APIs.

Then there’s memory: persistent storage of long-term facts or user preferences that lets agents personalise responses and “remember” over multiple sessions. Finally, tool use and function calling allow agents to trigger specific actions – booking a meeting, querying a database, even running code – in the middle of a conversation.

These components work together to create agents that aren’t just responsive, but proactive and adaptive.

Without well-structured context, even the most advanced language models stumble. You’ll get repetition, vague or generic answers, brittle reasoning and short-lived interactions that feel robotic. With context, those same systems can maintain coherence, deliver personalised responses, solve complex problems and behave more like collaborative partners.

Consider the difference between a chatbot that forgets everything between sessions and an assistant that knows your history, remembers your goals and understands your preferred tools. One is a novelty – the other, a game changer.

Context engineering tends to fall into four practical approaches:

1. Write: This involves storing context externally, such as temporary scratchpads during a session or long-term memories that persist across sessions. These become reference points the agent can use when responding or making decisions.
2. Select: The next challenge is deciding what context to load for a given task. This means fetching relevant facts, memory entries, tool descriptions or examples – and filtering out noise. Techniques like vector embeddings or schema-based retrieval help keep the focus sharp.
3. Compress: With token limits in place for most large language models, you can’t feed in everything. Summarisation methods (like recursive or hierarchical summarisation) help condense large volumes of data into useful, digestible inputs.
4. Isolate: Finally, it’s important to segment different types of context. Keeping tool descriptions separate from long-term memory or sub-agent logic avoids confusion or hallucination. It’s the difference between an agent that can think clearly and one that spirals into contradiction.

Some of the most exciting use cases are already showing the impact of good context engineering:

• Customer support agents that recall previous issues and combine that knowledge with internal documentation to deliver informed responses.
• Developer agents that work with specific codebases and project contexts to write or debug code more effectively.
• Data agents that query company dashboards and provide answers based on real-time or historical data.
• Personal assistants that learn from your habits, preferences and past interactions to streamline tasks.

In each case, the agent becomes more than just a tool – it becomes a capable digital co-worker.

As foundational as context engineering is, it’s not without its challenges. The most pressing include:

• Token limits: Most large language models (LLMs) operate within a fixed context window. If prompts or data inputs exceed that limit, critical context can be lost or truncated – leading to degraded performance.
• Hallucinations: When LLMs lack clear or accurate context, they can fabricate information, creating misleading or entirely incorrect outputs. This often happens when irrelevant or conflicting context is introduced.
• Context drift: Over long interactions, especially without persistent memory, agents may lose sight of user goals or task objectives. This makes maintaining state continuity a vital part of any context-aware system.

Managing these challenges is part of the art and science of context engineering – and what separates average agents from truly intelligent ones.

To fully appreciate the power of context engineering, it helps to compare it to its predecessor: prompt engineering.

Let’s say you're building a legal assistant. A prompt engineering approach might look like this:

“You are a legal assistant. Summarise this contract in plain English.”

The model might deliver a generalised answer, based purely on the text it receives. But now, contrast that with a context engineering approach:

• The system retrieves the relevant legal context, such as jurisdiction-specific laws or previous interactions with the user.
• It uploads the contract from a document store and attaches prior cases, preferences or instructions.
• It sends this full context – not just the raw prompt – to the model, wrapped in task-specific metadata.

The result? A smarter, more personalised and more accurate output that reflects not just what the user asked, but how they want it answered.

If you’re planning to build a context-aware agent, start with the fundamentals:

1. Define the goal: What is the agent meant to do? Be specific.
2. Map your sources: What types of information are needed to succeed?
3. Choose how to structure it: Use schemas, chunking and embeddings to shape the data.
4. Design the flow: Determine when and how the agent should retrieve or inject context.
5. Test and iterate: Compare performance with and without context, and optimise.

As we move deeper into the age of intelligent agents, the goal isn’t just smarter responses – it’s deeper collaboration. Whether it’s helping a designer ideate, a developer code or a compliance officer navigate regulations, these agents will act as copilots who truly understand their domain.

Just as data engineering powered the rise of modern analytics, context engineering will underpin the next generation of intelligent, helpful, human-centric AI.

At its heart, context engineering is more than a technical process – it’s a design philosophy. It’s about building systems that understand before they answer, and remember before they respond.

If you’re building AI agents, keep this in mind: without context, you have a chatbot. With context, you have an agent.