The context engineering maturity model

The urgency around building a context engine is driven by the rapidly changing capability profiles of AI agents themselves and the specific ways this growth interacts with the quality of context infrastructure.

These improvements represent a step change in what we can ask agents to do. An agent that can operate for two minutes with 50% tool accuracy is useful for narrow, short-horizon tasks with low stakes. An agent that can operate for an hour with 90% tool accuracy can complete complex, multi-step workflows across many systems, make many interdependent decisions, and take many consequential actions—all without human oversight.

As a result, the quantity and diversity of use cases deepen and broaden. The character of what we can do with AI changes drastically. But this change also shifts our constraints.

When an agent can reason autonomously for an hour and execute hundreds of tool calls with high reliability, the quality of what we give it to reason about becomes the primary determinant of outcome quality.

Data and agent-ready data are not the same thing

Most enterprise data architectures were designed for human users navigating dashboards, batch reports, API-to-API integrations with documented contracts, and analytics workloads that tolerate some staleness. Not agents.

Agents have different requirements. They need to navigate relationships dynamically, understand which piece of information is most relevant to a specific decision, and trust that what they read reflects the current state of the world.

Most organizations recognize the gap, but many underestimate just how wide it is. They often only grasp its true scale when it surfaces through production failures, such as:

• Agents that confidently answer the wrong question
• Agents that produce answers that were accurate as of last week
• Agents that cannot connect an account record to its associated opportunities without the developer having hand-coded that join logic into the prompt.

And even these failures don’t cover it. There’s an invisible opportunity cost to running limited agents: what could you be accomplishing if your agents worked better?

The window to act is narrow

Organizations building AI products in production today are setting the architecture patterns that will define the next decade of enterprise AI infrastructure. Teams that adopt open standards like MCP now will be positioned to avoid expensive replatforming later. Organizations that wait will pay the cost of migration on top of the cost of catching up.

The compounding nature of context infrastructure makes the opportunity asymmetry particularly significant. A context system that has accumulated memory, personalization signals, and organizational knowledge for, say, two years might be effectively ten years ahead because the value of that accumulated context compounds in ways that are not easy to replicate from scratch.

Remember, too, that the OpenAIs and Anthropics of the world are still developing new models. You can’t predict what will emerge next, but the sooner you have a context engine ready, the faster and more easily you can plug the latest model in.