AI Context Engines: The Next Evolution of Context Engineering

AI applications and agentic systems are only as good as the context they’re given: the relevant information, data, and situational details these systems need for interpreting inputs and responding accurately.

The practice of context engineering arose in 2025 as a way to systematically optimize the data provided to AI agents and applications, particularly in production systems. In 2026, though, it is becoming clear that context engineering itself is only part of the solution. These agents and apps also need context engines.

Context for AI apps and agents is not raw data sitting in a single table. It consists of business data objects like a "customer profile" or an "order summary." These are composite constructs assembled by pulling data from operational tables, joining them together, applying business rules (e.g., flag any order over $10K), and aggregating values across different systems. So, for example, data from a CRM, a billing system, and a support ticketing platform into one unified view: a business object named customer.

Business objects aren’t just related data crammed together, though. They are derived from underlying source data through applied processes like joining, filtering, and aggregating. Because any business object is not a primary input but a unique computed output, it exists as derived state.

State is absolutely crucial for business objects used as AI context. Because they are derived from underlying inputs that are themselves subject to change, business objects are only accurate when those inputs are current. Whenever something upstream changes, any derived business object needs to update too — or else the object becomes stale and the AI system is operating on outdated context.

Context isn't static. It's a living, computed thing that must stay in sync with reality, and maintaining that derived state requires more than a database optimized for storing rows or scanning history. Above and beyond context engineering, AI context requires a system that continuously assembles and maintains the current shape of the business, live, as data changes: in other words, a context engine.

A context engine is an operational data system designed to deliver stateful business objects as inputs for AI context. Like AI agents, context engines don’t just store data — they act on data.

A context engine system produces live business objects: derived datasets built from multiple sources, kept current as those sources change, and served directly to the systems that act on them. APIs expose these objects, applications display them, and automation workflows and AI agents use them as living context for taking actions and making decisions.

Context engineering is a practice, designing architecture to feed an LLM the right information at the right time. It's about building the data pipelines that connect a disconnected model to external data and information to ground its responses in facts, not just training data, and overall has been the right first step. However, context engineering does not inherently address state.

AI agents are autonomous systems that observe data, make decisions, and take actions that include writing back to systems (for example, updating inventory, approving transactions, and adjusting prices). This creates a loop: the agent acts, then needs to see the results of that action to decide what to do next. The tighter this loop — and the faster an agent can see the effects of its actions — the more effective the agent..

A single agent interaction can trigger dozens of reads and writes that quickly fall out of sync with system state, rapidly aggregating into context bloat. Agentic data systems need infrastructure that can process agent-scale writes in real-time while keeping agent-scale reads current even as data is continually changing.

Because context engines are built for instant response to continual changes, they are ideal for agentic data architectures. Context engines produce live business data objects that serve as fresh, correct, and tightly-tailored relevant context that AI applications and agents can consume directly (and with high efficiency).

A context engine system provides fast access to fresh, integrated context in the form of live business data objects that agents can query and discover over MCP. These objects are always correct and up-to-date, but must be created and continually maintained within a live data layer.

The hard part of taking an AI initiative from pilot into production isn't the LLM. It's the data.

AI systems need fresh, integrated context to make good decisions, served fast enough for them to reason and act, but this is challenging — or even impossible — to achieve with the traditional data infrastructures many teams are still building with:

• Operational databases are where the freshest data lives, but they weren't designed for the kinds of context agents need. Agents end up wasting time and tokens assembling and transforming the data themselves, instead of solving the actual business problem.
• Data warehouses have the kind of integrated, well-modeled data that agents demand, but there’s built-in latency. Data that might be minutes or hours old is simply unusable for agents that need to react to changing conditions.
• Stream processing frameworks can keep data fresh, but they are cumbersome: engineers have to write code in domain-specific languages, manage state across distributed systems, and handle failures manually. They’re also expensive to build and difficult to change whenever business requirements shift.

As systems that serve continuously updated, query-ready data to modern applications and AI agents, live data products — pre-computed business objects like Customer, Order, or Inventory, assembled from multiple operational sources — require three interdependent and non-negotiable properties: freshness, correctness, and composability.

Freshness means reflecting current reality, not a recent snapshot. Correctness means handling updates, deletes, and transactional boundaries so downstream consumers never see partial or inaccurate state. Composability means derived views — the layered, query-ready representations built on top of those data products — can stack on one another without introducing timing gaps or stale intermediate layers.

For both live data layers and context engines, these requirements intersect and reinforce each other: data that is fresh but not correct leads to errors or agent process failures. Data that’s correct but stale makes downstream systems go astray. And data that is composable but inconsistent propagates errors throughout any views that depend on it. The traditional data infrastructure options we’ve come to depend on ultimately fail one or more of these scenarios.

Materialize takes a different approach: pre-compute context and keep it live, so it's always fresh and can be queried in milliseconds. Which makes Materialize a plug-and-play context engine for operational workloads in agentic data infrastructures:

1. Operational data feeds into Materialize, where it gets joined and transformed into data products (like Customer, Order, or Inventory).
2. Agents discover and query these data products via MCP, getting results in milliseconds because everything is pre-computed and kept continually live and current with actual system state.
3. When an agent takes an action, like updating inventory or approving a transaction, the data products it accesses reflect the change immediately. Agents have instant results they can observe and use to quickly course-correct if necessary.

Materialize continuously maintains pre-computed business objects that reflect the current state of upstream data sources, so agents and applications can query rich, integrated context in milliseconds without assembling it on demand. Because Materialize processes changes incrementally as they arrive and preserves transactional consistency across layered views, the context it serves is always fresh, always correct, and composable without coordination overhead. This makes it a natural infrastructure layer for AI applications and agentic systems that operate in tight observe-decide-act loops that depend on fresh, correct data.

Context engineering was the right first step. It moved beyond prompt construction to establish the discipline of systematically designing how, when, and what data an AI system receives. But context engineering is a practice, not infrastructure. It can design the ideal context an agent should receive without guaranteeing that context is fresh, correct, or composable at the moment it's needed.

The gap between engineering context and serving it reliably is where most production AI systems struggle today. Agents that read stale data make bad decisions. Agents that see partial updates experience context drift and lost confidence in their own outputs. Agents that can't compose business objects across systems waste cycles on coordination instead of problem-solving. Solving all of these problems comes down to the same place: infrastructure.

Context engine systems built on live data layers are the AI infrastructure link that’s been missing. They maintain derived state continuously, so that the relevant, pre-constructed business objects that agents depend on are always current, always consistent, and always ready to query. Rather than assembling context at request time from scattered, variably-fresh sources, a context engine ensures that the work of joining, transforming, and maintaining data is done ahead of time.

Agentic systems don't just consume context once. They operate in loops: observing, deciding, acting, then observing again. Every pass through that loop demands context that reflects the current state of the world, including the effects of the agent's own prior actions. Context engineering describes what agents need. A context engine delivers it.

Materialize is a platform for live data mesh architecture and agent-ready digital twins, using only SQL, and it’s the ideal power train for a context engine. It is built around a breakthrough in incremental-view maintenance, and can scale to handle your most demanding agent-scale context production workloads. Deploy Materialize as a service or self-manage in your private cloud.

We’d love to help you make your operational data ready for AI. Go to materialize.com/demo/ to book a 30-minute introductory call.