We’re living in an exciting time for artificial intelligence (AI). You’ve probably heard about AI that can “think” and act on its own – this is called agentic AI. Think of it like a super-smart assistant that can find information, use tools, and figure things out to get a job done. But for these AI agents to be truly useful and reliable, they need something crucial: context. And getting that context right is where a new skill called context engineering comes in.
Imagine asking a regular AI assistant a question. It might give you a good answer based on what it was generally trained on. Now imagine asking an agentic AI. It needs to understand *your specific situation*, *your company’s data*, and *exactly what you want it to do*. This deeper understanding, this specific information, is the context. Without it, the agent can’t be truly helpful; it might give wrong answers or miss the point entirely.
Companies are realizing that to make these agentic AI systems work, they need to connect them to all the hidden data scattered across their businesses. This includes documents, emails, customer feedback, internal reports – you name it. As we look towards 2026, solving this data connection problem is going to be a huge deal for how widely agentic AI is used around the world. Ken Exner, the chief product officer at Elastic, puts it simply: "People are starting to realize that to do agentic AI correctly, you have to have relevant data. Relevance is critical in the context of agentic AI, because that AI is taking action on your behalf. When people struggle to build AI applications, I can almost guarantee you the problem is relevance.”
The push for agentic AI is gaining serious momentum. Companies are scrambling to get ahead or find new ways to be more efficient. A study by Deloitte predicts that by 2026, more than 60% of big companies will be using agentic AI in a major way, moving beyond just trying it out. On top of that, researcher Gartner forecasts that by the end of 2026, a massive 40% of all business applications will have these specialized AI agents built into them. This is a huge jump from less than 5% in 2025! This means AI assistants are evolving from simple chatbots to AI agents that deeply understand their surroundings and tasks.
This growth isn’t just about new fancy tech; it’s about unlocking new levels of productivity and innovation. When an AI agent can understand your company’s specific data and take action, it can automate complex tasks, provide insights no human could easily find, and offer truly personalized experiences. The key to making all of this happen reliably is ensuring the AI agent has the right information at the right time.
So, how do we give these AI agents the information they need? This is where context engineering steps in. It's the process of carefully gathering, organizing, and feeding the right data to AI agents so they can perform their tasks accurately and effectively. It’s not just about dumping data into the AI; it’s about making sure the AI understands what data is important, where to find it, and how to use it.
Context engineering helps the AI agent not only get the data it needs for deep, accurate answers but also helps the underlying large language model (LLM) understand which tools to use to find that data and how to operate them. While there are new standards like the Model Context Protocol (MCP) that help LLMs talk to external data, it’s still challenging for companies to build AI agents that can use their own private data and combine data retrieval, security rules, and task execution all in one place, seamlessly.
Platforms like Elastic are stepping up to fill this gap. Elasticsearch, a well-known platform for managing data, has introduced a new feature called Agent Builder. This tool aims to make the entire process of creating and managing AI agents much simpler. From building them, setting them up, running them, customizing them, and checking how they're working – Agent Builder tries to cover it all.
Agent Builder helps create agents that can work with private data using different methods. It can use tools like Elasticsearch Query Language, which is a powerful way to find, change, and analyze data. It can also use workflow modeling to define how the agent should act. Users can then combine these tools with specific instructions (prompts) and an LLM to build their agent. Elastic offers a ready-to-go conversational agent that lets you chat with your indexed data, or you can build your own from scratch. As Exner says, "Data is the center of our world at Elastic. We’re trying to make sure that you have the tools you need to put that data to work."
The field of making AI understand and use information is evolving rapidly. We’ve moved from prompt engineering (crafting the right questions for AI) to retrieval-augmented generation (RAG), where information is fed into the AI’s short-term memory (the context window). Now, we’re seeing the rise of protocols like MCP that help AI agents intelligently select tools and find data.
But this evolution isn’t stopping. Exner predicts, "Given how fast things are moving, I will guarantee that new patterns will emerge quite quickly. There will still be context engineering, but they’ll be new patterns for how to share data with an LLM, how to get it to be grounded in the right information." The goal is to make it possible for LLMs to understand and use private data they haven't been trained on, leading to more accurate and reliable AI applications.
Context engineering is becoming a recognized discipline. It’s not something that necessarily requires a computer science degree, but more training and best practices will emerge. As Exner notes, "The thing that people will have to figure out is, how do you drive automation with AI? That’s what’s going to drive productivity. The people who are focused on that will see more success." This focus on driving automation through AI is what will truly unlock the power of agentic systems.
The rapid adoption and advancement of agentic AI, powered by robust context engineering, will have profound implications across industries:
Gartner’s prediction that 40% of enterprise applications will incorporate task-specific agents by 2026 signals a major shift. This means that AI will no longer be an add-on but an integral part of how businesses function. Imagine AI agents handling:
As agentic AI gains access to sensitive proprietary data, robust data governance becomes non-negotiable. The challenge isn't just about *accessing* data but doing so securely and compliantly. This involves:
Addressing these challenges is as important as the technical development of the agents themselves. Organizations must build frameworks that govern the AI's interaction with data, ensuring trust and responsible deployment.
The shift from RAG to more sophisticated context engineering for agentic AI represents a significant leap. While RAG helps LLMs access external documents, it's often a more passive process. Agentic AI, on the other hand, can actively decide *what* information it needs, *how* to get it (e.g., by querying a database, calling an API, or summarizing a report), and then *how to use it* to achieve a goal.
This active, intelligent data retrieval and utilization is what empowers agents to perform complex, multi-step tasks. The development of standards like MCP is crucial here, enabling agents to communicate effectively with various tools and data sources, much like a human expert would navigate different resources to solve a problem.
For businesses looking to harness the power of agentic AI, the message is clear: focus on your data and how to connect it effectively.
Agentic AI represents the next frontier in artificial intelligence, promising unprecedented levels of automation, efficiency, and intelligence. However, its true potential hinges on our ability to provide it with the right context. Context engineering is not just a technical process; it's a strategic imperative that bridges the gap between raw data and actionable intelligence. As organizations move towards widespread adoption by 2026, those that master context engineering will be best positioned to lead the charge, unlocking the transformative power of AI agents and redefining what’s possible in business and beyond.