The new skill in AI is not prompting, It's context engineering

Context Engineering is new term gaining traction in the AI world. The conversation is shifting from "prompt engineering" to a broader, more powerful concept: Context Engineering. So now the emphasis is more on providing the correct and complete context for the LLM to accomplish the task, rather than picking a good prompter

With the rise of Agents it becomes more important what information we load into the “limited working memory”. We are seeing that the main thing that determines whether an Agents succeeds or fails is the quality of the context you give it. Most agent failures are not model failures anymore, they are context failures.

What is the Context?

It's important to realize that context is not just a prompt you send, but something much bigger and more complex. Context has many components, from the prompt you send to the structure of the response. Let's take a closer look at these.

• Instructions / System Prompt - A predefined set of rules and guidelines that shape the model's behavior throughout the conversation. Often includes examples, tone instructions, and constraints to ensure consistent and aligned responses.
• User Prompt - The immediate input or request from the user — a question, command, or task the model is expected to respond to.
• State / History (Short-Term Memory) - The running transcript of the current interaction, including all previous user messages and model responses. This provides immediate conversational context.
• Long-Term Memory - Persistently stored knowledge from past interactions — such as user preferences, project summaries, or important facts the model has been asked to retain for future use.
• Retrieved Information (RAG) - Relevant external knowledge dynamically pulled from documents, APIs, databases, or search systems — often used to supplement the model’s core knowledge with up-to-date or domain-specific data.
• Available Tools - A list of functions or capabilities the model can access or execute — for example, check_inventory(), send_email(), or perform calculations — enhancing its ability to take actions.
• Structured Output - Specifications for formatting the model’s response in a machine-readable structure — such as JSON, XML, or custom schemas — to support downstream processing or integrations.

Why smart AI agents aren’t about code alone

When you're building an AI agent, the most important part is not the code — it’s the context you give the model.

Yes, it's easy to connect an LLM and get a response. But the difference between a basic prototype and a truly helpful agent comes from how much useful information the model gets before generating that response.

Let’s look at an example. Imagine your AI agent gets this message:

Hey, just checking if you’re around for a quick sync tomorrow.

A basic agent only sees this single message. It doesn't know your schedule, your past communication with the person, or how you normally write. Even if the code works correctly, the reply will sound robotic:

Thank you for your message. Tomorrow works for me. May I ask what time you had in mind?

Now let’s see what happens when the agent has rich context. Before sending anything to the model, it collects:

• Your calendar (which shows you’re fully booked tomorrow)
• Past emails with this person (you usually use a casual tone)
• Your contact list (this is an important partner)
• Tools like send_invite() or send_email() that it can use

With that context, the model can generate a much better response:

Hey Jim! Tomorrow’s packed on my end, back-to-back all day. Thursday AM free if that works for you? Sent an invite, lmk if it works.

Same model. Same basic code. The only difference is the context.

A good agent doesn’t just ask the model to answer — it prepares the model with the right information to make the best decision. That’s what makes the experience feel smart and helpful.

So, when an AI agent fails, it’s often not because the model isn’t good enough. It’s because it didn’t get the context it needed.

From prompt engineering to context engineering

What is context engineering? While prompt engineering is about writing the perfect input for the model — a single, well-crafted string of instructions — context engineering goes much further.

Put simply:

Context Engineering is the process of designing dynamic systems that deliver the right information and tools, in the right format, at the right moment — giving the model everything it needs to complete a task effectively.

It’s a shift in mindset. Instead of focusing only on the text prompt, you focus on the entire environment around the model.

Here’s what makes context engineering different:

🧠 It’s a System, Not Just a String

Context isn’t just a fancy prompt template. It’s the result of a system that runs before the model is called. This system decides what data or tools the model needs, then builds the right input on the fly.

⚙️ It’s Dynamic

The context changes depending on the task. If the user wants to book a meeting, the model might need your calendar. If it’s summarizing a report, it might need to fetch documents. Every request has different context needs.

🧩 It’s About Providing the Right Info and Tools, at the Right Time

The model should only get what’s useful and necessary. Too little context — and the model makes wrong guesses. Too much — and it gets overwhelmed. The job of context engineering is to get that balance right.

📐 Format Matters

How you present information is just as important as what you present.

– A short summary is better than dumping a full document.

– A clear tool definition is better than a vague description.

– A structured format (like JSON or schema) helps the model reason more reliably.

In short, context engineering isn’t just about crafting inputs — it’s about building systems that support reasoning. The better your system builds the context, the smarter and more helpful your agent becomes.

🎉 Conclusion

As we move into the age of AI agents, it’s becoming clear that great performance doesn’t come from clever code alone. It comes from how well you prepare the environment around the model.

Context is no longer just an input — it’s the foundation. Whether you’re designing a chatbot, a smart assistant, or a decision-making agent, your success depends on what the model sees before it thinks.

So if your agent isn’t working the way you expect, don’t just blame the model or tweak the prompt.

Look deeper. Ask yourself:

• Does the model have access to everything it needs?
• Is the context clear, relevant, and structured?
• Are the right tools and knowledge available — and only when needed?

Context engineering is how we bridge the gap between basic prototypes and real, reliable AI systems.

As developers, it’s time we stop thinking only about what to say to the model, and start thinking about what the model needs to know.

Also here is a really good article about context engineering and I highly recommend it - Context Engineering for Agents, it is more complicated, but has a really useful info in it!