Why We Saw Context Engineering Coming Before It Became a Buzzword
Before context engineering became the industry buzzword it is today, we wrote about it - though we didn't call it that. Our blog (Are AI Agents Nothing But Multiple RAGs?) from early 2025 (to be more specific - Jan 4th, 2025) explored the very essence of what context engineering means, even if the term hadn't yet taken hold.
Let me take you back to our journey. In early 2024, we were deep in building RAG applications. By the second half of the year, AI agents exploded in popularity. But when we started experimenting with agents, something interesting happened: we weren't surprised. Agents felt like a natural evolution because we had taken the time to understand the foundations - what LLMs actually are and how RAG really works.
Here's what we learned, and why it matters.
The most important word in RAG (Retrieval Augmented Generation) isn't Retrieval - it's Augmentation. Every maxim about "garbage in, garbage out" traces back to this single principle.
After building multiple RAG applications, we saw a pattern: the quality and accuracy of outputs were determined entirely by how and what we augmented the LLM with. By "external data," I mean everything - the prompt structure, the retrieved documents, the formatting, all of it. The LLM is stateless; it only knows what we tell it. Augmentation is that telling.
From RAG to Agents: The Same Foundation, Extended
When AI agents became the hot topic, we immediately recognized them for what they were: an extension of the same augmentation principle. Agents augment LLMs with tools and their outputs - enabling the LLM to dynamically fetch data from its operating environment.
But here's the crucial insight: LLMs are language models. They understand and generate language, nothing more. When we talk about "tools" in the context of agents, we're really talking about tool descriptions - textual schemas that the LLM processes to decide which tool to call and with what parameters.
The LLM itself doesn't execute anything. It simply generates language that specifies: "Call this tool with these parameters." The actual execution happens outside the LLM, in the agent system. The agent orchestrates a loop: the LLM decides which tool to use, the system executes it, the results are fed back as augmentation, and the LLM decides the next step. This continues until the LLM determines the task is complete.
This led us to a fundamental conclusion in our previous blog: AI agents are nothing but multiple augmented generations. They rely on the same principle as RAG, augmenting LLMs with external data, but they integrate multiple forms of augmentation: retrieved knowledge, tool schemas, execution results, reasoning traces, and observations.
Whether it's RAG or agents, the core challenge remains the same: understanding what to augment the LLM with, and how to do it effectively.
Then Came the Industry Validation Moment
While we were thinking about augmentation from first principles, the industry was catching up. In mid-2025, a wave of influential voices began coalescing around a new term: context engineering.
Tobi Lutke, CEO of Shopify, described it as "the art of providing all the context for the task to be plausibly solvable by the LLM."
Ankur Goyal emphasized that "context engineering is hard because it is pervasive. You need to engineer every layer of the stack to capture and make context available."
Walden Yan from Cognition AI wrote extensively about how context engineering is at the core of reliability in AI agents, stating that "at the core of reliability is Context Engineering."
And then Andrej Karpathy popularized the term further, calling it "the delicate art and science of filling the context window with just the right information for the next step," adding that it encompasses "task descriptions and explanations, few-shot examples, RAG, related (possibly multimodal) data, tools, state and history, compacting..."
What struck us wasn't that a new concept had emerged - it was that the industry had formalized what we'd been practicing all along. Context engineering wasn't a revelation for us; it was validation.
What is Context Engineering?
Let's establish a clear definition:
Context engineering is building dynamic systems to provide the right information (instructions, examples, tools, etc.) in the right format such that the LLM can plausibly accomplish the task.
This definition reveals three critical dimensions:
Context Engineering is a System: Agents don’t work from a single prompt. They pull context from multiple places - what the developer defines, what the user provides, what happened earlier in the conversation, tool outputs, and external data. Bringing this together is a system, not a one-off instruction.
The System is Dynamic: Context isn’t fixed. It changes as new inputs come in, so the way we construct prompts has to change too. A well-designed system adapts to the situation rather than relying on something static.
The Right Context Matters: Many agentic systems fall short because they don’t have the right information. LLMs need context to work effectively - if the input is incomplete or irrelevant, the output will be too. This is the same principle we’ve seen in RAG: garbage in, garbage out. Context engineering simply extends that principle into a more structured approach.
Why Context Engineering Matters
When agents fail, it’s easy to blame the LLM. But if we look from first principles, there are really only two reasons:
The model itself isn’t strong enough.
The model didn’t get the right context.
As models keep improving, the second reason is what causes most failures. The context provided is often incomplete or poorly structured.
Missing context: The model can’t guess what it doesn’t know. If you don’t give it the right information, it can’t make the right call.
Poor formatting: How you present information matters. A clear, structured input is far more effective than dumping in unorganized data.
This is the same lesson we saw with RAG: the quality of augmentation drives the quality of output. Context engineering takes that principle further by treating augmentation as a deliberate, systematic practice.
Conclusion: Driven by Why, Powered by Patterns
Context engineering didn't change our approach - it validated it. We've been thinking about augmentation, about what information LLMs need and how to provide it, since the beginning. We recognized that whether you're building RAG systems or AI agents, the fundamental challenge is the same: understanding what to put in the context window and how to structure it.
The industry now has a name for what we've been doing: context engineering. But the underlying principle remains unchanged. Success with LLMs comes from deep understanding, not surface-level pattern matching.
This echoes our motto at Innowhyte: Driven by why, powered by patterns.
We're driven by why - understanding the fundamental mechanics of how LLMs work, why augmentation matters, why context determines output quality. This first-principles thinking is what allowed us to anticipate context engineering before it became a buzzword.
We're powered by patterns - recognizing that RAG, agents, and future LLM applications all follow the same underlying pattern of augmentation. By understanding these patterns, we can build robust, reliable systems that adapt as the technology evolves.
Context engineering is important, and we're glad the industry has formalized it. But at Innowhyte, we'll continue doing what we've always done: thinking deeply about fundamentals, building from first principles, and creating deterministic AI systems that deliver consistent, meaningful outcomes.
At Innowhyte, we help teams enhance their agents with advanced context engineering and evaluation-driven design. Let’s talk - connect@innowhyte.com.