There’s a reason your AI agents aren’t surviving the jump from prototype to production. It’s just not the reason you think.
Let’s consider a common scenario. After a rigorous testing period, you’ve finally shipped a key set of AI agents to production. Yet users don’t seem happy with the actions the agents are taking.
You and your team dig into the logging data. You use the agents yourselves. You’re forced to agree with your users. Something feels off. In some fundamental ways, the agents don’t seem to understand the business.
The thing is, you know that information about your business is within your business. You’ve seen it. But your agents don’t seem to know it exists. Or maybe they do, but they’re not interpreting it correctly.
In a word, your agents lack context. This lack of context has created a demo-to-production gap that’s becoming the single biggest issue keeping agentic workloads from succeeding in the real world.
Why do agents keep hitting this problem? Because it exposes an intelligence gap.
Agentic success isn’t about making sure an agent has access to one or two data sources. It’s about providing an architectural context layer that makes business intelligence available to AI. In this article, we’ll look at what this layer consists of and how to add it to your existing architecture without completely rewiring your data stack.
Context proves that general intelligence isn’t enough
Fundamentally, Large Language Models (LLMs) have unlocked a new model of computer/human interaction. Trained on large quantities of generalized data, they can take human input and respond with human output.
But there’s also an important limitation. This generalized knowledge is insufficient for specific use cases. It needs access to the details of how your business works.
This is context in action. In its raw form, context can be drawn from many types of information, including user data, analytics, metrics, documentation, and filings such as annual financial reports. This information has to be processed, disambiguated, and made available to the AI as business intelligence.
This business intelligence has always been important. Now, with AI agents making autonomous decisions on our behalf, the importance of rich, accurate context is higher than ever.
The uncomfortable truth about context
Providing this context is about more than providing access to raw data.
Context is everywhere (and nowhere) in your enterprise
The data that provides context is scattered across your enterprise. Some of it may have been migrated to data lakes and lakehouses where it’s readily available. A lot of it is likely still languishing in data silos in a form that’s incompatible with the rest of the company’s data.
Most companies don’t know exactly how much data they have that they can turn into context. Even if they know where it is, the agents that need access to that data may not have a reliable way to find it when they need it.
Data takes many different forms
The format of the data you draw context from is also diverse. It can be structured, semi-structured, or unstructured.
Whatever format it takes, though, this data needs to be converted into business context in a meaningful, consistent way. Four different AI agents that access raw unstructured data might draw four different conclusions. An agent that finds two conflicting metric definitions in two different data warehouses often has no way to distinguish which one is the real value needed by the business.
Context needs to scale
Pulling the rich context that AI agents need to succeed together requires a scalable approach. It can’t be just up to one team attempting to centralize the entire organization’s data into a single data lakehouse. That makes one team the bottleneck for your company-wide AI efforts.
What’s needed instead is a balance between scalability and governance. Teams own their own data. The company monitors and enforces security and compliance. Agents discover context through a centralized repository that provides data regardless of where it resides, whether in the cloud or on-premises.
Why the context layer is the architectural element your AI workloads are missing
The problem is that teams are trying to solve the context problem in a one-off fashion, workload by workload. This isn’t enough to give agents the business intelligence they need to make accurate, responsible decisions on behalf of human actors.
Enabling context for all of your workloads requires a single enterprise context layer. This context layer applies common definitions, metadata, and policies so raw data becomes a shared ground truth for both human stakeholders and AI agents.
This context layer serves as your agentic control plane. It provides the grounding agents need to make accurate decisions by delivering scalable business intelligence with enterprise guardrails. That, in turn, fosters the trust required to bridge the gap from human intelligence to AI-powered decision-making.
An architectural context layer consists of two interdependent components.
Federated access to data
Most AI context solutions require companies to wait on a months-long data centralization project to feed the context their AI solutions desperately need. This is a pattern that forces your business teams and AI agents to wait months for the answers they need today.
Data federation enables your context layer to connect to any data from anywhere across the organization. Instead of centralizing data and metadata in a single location, you can surface data where it already lives, today, where it’s already being used successfully and tuned for high performance.
This doesn’t mean you never centralize data. You may still need to bring some data sets into modern open table formats, such as Apache Iceberg, for better performance or governance. With a federated approach to data, you can make such centralization decisions selectively, based on usage data. Centralization becomes a choice, not a mandate.
Data products as the base unit of context engineering
Your AI agents need more than raw data. They need context that’s engineered, provided in a format that contains everything needed to identify, use, and govern the underlying data. The data world has long needed a consistent way to treat data as a product, providing a single, packaged, manageable unit.
This is where data products come in. Data products are a single way to describe and package data that promotes data quality, improves context, and enables collaboration across teams.
Data products contain the four components necessary to make context available to AI agents in a scalable fashion:
- Data: The underlying data in whatever format it currently exists, whether that involves relational tables, NoSQL, JSON, Excel, PDFs, etc.
- Metadata: The technical and business context that describes the data. This means the data owner, last modified dates, data types, keywords, documentation, business purpose, etc.
- Business logic: The business rules enforced around accessing and maintaining the data, along with the tools, API endpoints, and so on, used to access and manipulate it.
- Governance and access: The data lineage relationships, access controls, security rules, compliance guidelines, legal regulations, and approved access request pathways needed to manage who can see what data
Data products put your data in a form that’s ready-made for AI workloads. Metadata ensures that data is fully described so that agents know its proper use and business purpose. Business logic provides access methods that are discoverable dynamically by agents via Model Context Protocol (MCP). Governance and access guarantees that data products follow all applicable guidelines around data quality and security.
The combination of data federation and data products provides a scalable architectural context layer. Teams continue to maintain data for their part of the business, while the company centrally monitors and ensures governance.
The context layer grounds AI agents in reusable intelligence
AI agents need more than a semantic layer, a data catalog, or a single silo of metadata. They need access to reusable intelligence, a single layer of cross-domain assets that power analytics, data-driven applications, and AI.
The context layer is indispensable for grounding your AI agents. It provides a structure for:
- Harvesting live metadata from your existing data infrastructure
- Structuring this data into business semantics
- Assembling scoped context for each question that an AI agent asks
- Serving that context to the agent before it begins its reasoning process
Once put into place, the reusable intelligence provided by the context layer quickly compounds. Whereas traditional data projects create isolated assets, every AI-ready data product added to a context layer creates new business context, relationships, governance, and business logic.
This foundation accelerates every new data project, whether analytics, applications, or AI. It reduces time to market, enabling teams to ship sooner with fewer errors.
Eventually, every AI agent will need a context layer to be successful. Without one, you’re relearning every lesson with each new data project. With one, your teams have everything they need to ship production-ready solutions on top of a proven foundation.
Starburst is an enterprise intelligence platform that supports all the foundational elements needed to make agentic AI work in an enterprise setting. Contact us today to learn more about how to make it work for your business.
