Context Engineering for Everyone: Part 1

Two years ago, the industry predicted that prompt engineers would take over the world. This conjures up images of people typing incantations into LLMs, in search of that “perfect prompt” to get the best outputs. Unfortunately, we discovered that though modest wording changes of messages do help LLMs perform better, the gains aren’t enough to justify an independent job. This poisoned the well for the term prompt engineering.

But just as alchemy eventually gave way to chemistry, we are seeing the “magic prompt” era evolve into something more rigorous. In 2026, the gap between a fragile proof-of-concept and a production-ready agent will be in the engineering of the context.

The System is the Solution

High-performing agents depend on a sophisticated information flow. Even the most capable LLMs are bottlenecked by the environments into which they are deployed. By refining the total context – everything from data schemas to retrieval logic – you are able to move beyond simple “chat” into the realm of task execution. We’re already seeing this in action: context-engineered harnesses have more than doubled the output of standard LLMs, pushing them past human benchmarks on complex reasoning tests like ARC-AGI-2.

💡The Poetiq Breakthrough: For years, the ARC-AGI benchmark was “unsolvable” for AI because it tests logic rather than memorized data. In Dec 2025, Poetiq reached a score of 55% (when the average human scores at 60%). They didn’t achieve this with a new model; they built a specialized “harness” that forced the LLM to verify its own logic and remove noise. Through context engineering, they doubled the intelligence of existing foundation models.

So what is Context Engineering?

Context engineering is the deliberate crafting of the inputs to an LLM as part of an agentic workflow: the system instructions, the tools, the tool descriptions, the tool schema, what the tools output, length of the context, compaction, and more. Excluding user messages, the totality of the input to the LLM should be carefully managed and optimized for the best agent performance.

Everyone should be a prompt engineer in 2026 to perform their job optimally, from developers using a coding assistant to write code, to content creators using AI systems to bring their ideas to life. Behind the scenes, context engineers set those agents and prompts for success. Key pillars to context management are:

• Providing the best tokens.
  • Providing accurate and comprehensive system prompts.
  • Tools that grab the best token. Models have gotten smart enough that they can effectively find the best tokens when given the right instructions.
• Managing context size. 2025 has seen an exponential increase in model capabilities and intelligence, but not an exponential increase in the amount of context they can consume.
  • Small, fast, dumber models can move through tokens more quickly to bring the juiciest tokens back to the smart model without context size bloat.
  • 2025 has proven out how effective compression/compaction can be to allow agents to run almost indefinitely on a problem.
• Monitoring, evaluating, and continually improving the agents. Managing drift.
  • Due to the jagged nature of model intelligence and the inherent difficulty of creating broad offline evaluations, getting context and the agent in the right place means improving and iterating on it as it works with real users and real data.

An agentic system designed to feed the right context to the LLMs will easily out perform an ill-designed system using the same LLMs. In fact, building and feeding the right context can enable smaller LLMs to perform at similar levels to large LLMs with bloated and irrelevant context.

Retrieval is still the Backbone

Vectara was built on the principle that information retrieval is the most important part of the AI stack. It is the ultimate signal-to-noise filter. There is a common misconception that larger context windows (the ability to feed an LLM millions of words at once) solve the data problem. In practice, it’s not just hard to scale an LLM’s context window itself, as context windows grow, the performance degrades and latency spikes. It is fundamentally difficult to scale a model’s raw intelligence and its “working memory” at the same rate.

Instead, the more effective architectural choice is to pair an intelligent model with a surgical retrieval system. This way, the model does not have to sift through a company’s entire database on every turn. A scalable retrieval system paired with an intelligent indexing pipeline that extracts information from complex multimodal files becomes a core part of an enterprise’s agentic solution.

Vectara takes your unstructured enterprise data and makes it instantly queryable via natural language. When an agent generates a question, Vectara provides the precise “tokens” required for the answer. This creates a lean, high-intelligence loop; the model stays sharp because it’s not drowning in irrelevant information, and the business stays secure because the data is accessed dynamically rather than baked into the model weights.

On top of that, Vectara also looks at the entire lifecycle of an agent’s interaction, focusing on the “side channels” that often get overlooked: how to compress context, what kind of information you pass to tools via side channels vs through the agent, what the system instructions say and its dynamic content, tool instructions, monitoring context changes and suggested improvements, guardian agent feedback to agents, and more.

Over the next few blog posts we’ll share the context engineering techniques we’ve discovered and how Vectara enables you to execute them to have world class agents. Stay tuned!