Context Engineering, Decoded: Why CMOs Need to Know Which Definition Their Team Means

Context engineering is one term running on two different definitions in 2026. The first is a technical engineering discipline for managing what AI sees inside its context window. The second is a marketing-strategy reframe arguing brands must structure their data and content for AI agents to consume. CMOs need to know which version their team means before funding either.

One Term, Two Definitions

Context engineering is showing up more frequently than “Ambient Realism” photography. You’ll find it in vendor pitches, board decks, and the State of Martech 2026 report. The term means two different things, and most CMOs hearing it can’t tell which one their team is being sold. One version is real engineering work that lives with technical teams. The other repackages governance and brand work the marketing function has done for decades, with a new label and a new pitch around it.

Both versions trace back to the same moment. Shopify CEO Tobi Lütke posted on June 19, 2025 that “context engineering” described the core AI skill better than prompt engineering (1. Willison, 2025). Six days later, AI researcher Andrej Karpathy (formerly of OpenAI and Tesla) quote-tweeted his agreement, framing the discipline as filling a model’s context window with the right information at each step (1. Willison, 2025). The technical press picked it up right away. Anthropic’s engineering team was publishing on it by September (2. Anthropic, 2025).

The term then arrived in marketing leadership. chiefmartec.com creator Scott Brinker and Frans Riemersma’s State of Martech 2026, released May 5, 2026, declared this the year of context engineering and introduced “context engineer” as a new marketing operations role (4. Brinker & Riemersma, 2026). Two disciplines. One label. Two funding paths.

Definition One: Technical Plumbing

The technical version of context engineering is real engineering discipline. Every AI tool runs against a fixed amount of working memory called the context window. Whatever doesn’t fit in that window is invisible to the model when it answers. Context engineering is the practice of deciding what goes in the window so the answer isn’t built on a guess.

In practice this means assembling system instructions, retrieved documents, tool outputs, conversation history, and persistent memory into a coherent input package, every turn, dynamically. Anthropic’s engineering team frames it as the work that determines whether an agent can reliably accomplish a task across many steps (2. Anthropic, 2025). Engineering practitioners describe the discipline as bringing software rigor to prompt assembly, with version control, testing, and observability built in (3. Osmani, 2025).

This work mostly lives in your AI tooling layer, not your marketing department. If your team has any custom AI agents in production, your engineers are doing context engineering whether anyone calls it that. It sits with the people who build and maintain the systems that consume the data.

The shorthand: prompt engineering asked how to phrase the question. Context engineering asks what the AI needs to know before you ask. That’s the version Karpathy and Lütke argued for, the version reshaping how serious AI applications get built. It’s not new in spirit. Retrieval and chatbot memory tools have been doing this work since 2020. The label is what’s new.

Definition Two: Marketing Strategy in New Clothes

The marketing version of context engineering says something different. State of Martech 2026 frames context engineering as the practice of assembling the right data, instructions, and tools so AI agents can act on your brand’s behalf or, increasingly, on your customer’s behalf (4. Brinker & Riemersma, 2026). The provocation in the report is that the buyer is changing. Customers are starting to send their own AI agents to research products, compare vendors, and execute purchases. When that happens, the agent skips your funnel. Your marketing job is to make sure that agent gets the right picture of you when it shows up.

This reframing has a kernel of truth. AI does expose martech complexity. When an agent is mediating a customer interaction, your messy data, disconnected systems, and fuzzy governance leak directly into the customer experience. That part is real (and it can make the customer experience suck).

The repackaging risk is what most of the surrounding work already was. Structured data for machine readers, governance, brand consistency across systems, single source of truth for product information. The marketing function has been doing this for decades. Calling it context engineering changes the sales pitch around the work. The work itself stays the same.

The framing tips its hand in the report itself. State of Martech 2026 includes the line “Prompt engineering is, like, so 2024” (4. Brinker & Riemersma, 2026). That’s buzzword cycling. The work underneath is real. The packaging around it is moving faster than the substance.

The new wrinkle is the agent-as-buyer scenario. That changes a CMO’s homework on how a brand shows up to non-human readers. The work matters regardless of what the discipline gets called.

What a CMO Does With This

Two definitions running on one term creates a budget problem. The technical version is engineering work. The strategic version is governance and brand work. Different owners. Different timelines. Different budgets entirely. Someone pitching a “context engineering initiative” to a CMO without clarifying which version they mean is selling fog.

The diagnostic question is short. Ask the people who own AI tooling: are you doing context engineering at the model layer, the data layer, or both? Ask the people who own brand and content: are you adapting brand assets so AI agents can read and use them, and how do you know that work is paying off? The answers tell a CMO which version their organization is funding, where the gaps are, and what the ask amounts to in dollars and headcount. The context rot ops playbook covers the operational side once the work is identified.

The term will fade. The work won’t. Funding the label burns budget without producing visible outcomes. Funding the work, on either side of the split, is what survives the next buzzword cycle.