Context-Aware AI Agents: Why Most Aren’t (and What It Takes to Build One That Is)

Most conversations about “context-aware” AI agents are actually conversations about something else. They’re about prompt construction, retrieval scaffolding, and clever ways to stuff more relevant tokens into a context window. That work matters, but it’s downstream of a problem most of the conversation skips: Whether the context being engineered is trustworthy, governed, and semantically coherent in the first place.

This is why so many enterprise AI agents demo brilliantly and break the moment a real analyst asks a real question. The agent has access to the data. It just doesn’t understand it.

The reframe this post will defend is straightforward. Context-awareness isn’t a property of the agent. It’s a property of the context infrastructure the agent draws from. The agents that work in production are the ones sitting on a governed context graph that was built before anyone called it AI.

What “context-aware” actually means for an enterprise data agent

A useful negative definition matters as much as the positive one: A context-aware agent is not retrieval alone. It is not RAG alone. It is not prompt scaffolding, and it is not an agent with a bigger context window. Each of those addresses a piece of the problem, and each of them, in isolation, produces an agent that retrieves the right-shaped tokens without understanding what any of them mean.

Context-aware agents sit at the intersection of three things: Governance, semantic infrastructure, and retrieval. Remove any one of them and the agent regresses to context-connected. According to the State of Context Management Report 2026, 82% of data leaders agree that prompt engineering alone is no longer sufficient to power AI at scale. The market is starting to feel the gap. It just hasn’t named it yet.

The question to ask of any agent isn’t “how clever is it.” It’s “what is it connected to, and is that thing trustworthy.”

Most “context-aware” agents are just context-connected

The industry uses “context-aware” to describe two different things, and the conflation is the source of most of the confusion.

• The first is context engineering, the prompt-construction discipline that decides what goes into the model’s working memory on any given turn. Anthropic’s recent piece on effective context engineering for AI agents is the clearest articulation of this discipline available right now, and it’s worth reading on its own terms. But it covers one layer of a two-layer problem. It assumes the context being engineered is already trustworthy. In most enterprises, that assumption is exactly the thing that doesn’t hold.
  
• The second is context management, which is the upstream question. Where does the context come from? Who certified it? Is it fresh? Does the agent know that “revenue” means gross in one schema and net in another, and which definition the current question is asking about?
  

An agent that retrieves your tables but doesn’t know which are certified, who owns them, what their quality scores are, or what their business definitions resolve to is context-connected, not context-aware. It has access without understanding. That distinction is the difference between an agent that ships and an agent that gets quietly turned off after the second wrong answer.

Both context engineering and context management are real disciplines, and they’re both necessary. But they operate at different layers, and treating them as the same thing is the mistake.

Context-aware vs. context-connected, side by side

The clearest way to make the distinction concrete is to walk the same agent through five capabilities and ask what happens at each stage when the underlying context layer is missing.

The conflict resolution row is worth lingering on. Pinterest‘s published version of that hierarchy is one of the most concrete answers anyone has put in public to “what does it mean for an agent to know which context to trust,” and almost nobody else writing about agent design is even asking the question.

The infrastructure layer most agent architectures are missing

Every capability in that comparison points to the same fact. None of it lives in the prompt. All of it lives in the context graph.

Lineage, ownership, certification status, business definitions, quality scores, freshness, the relationships between assets: These are not things an agent can reason its way to. They’re things an agent has to be told, and the only place they can be told from is a layer that sits underneath the agent and serves them on demand. That layer is the context layer. It is the load-bearing layer of any agent architecture that claims to be context-aware, and it is the layer most agent architectures don’t have.

The cleanest articulation of what that architecture looks like in production is the reference model DataHub published for a semantic layer for analytics agents, inspired by Pinterest’s approach.

Reading from the top:

• An agent access layer (MCP server, REST and GraphQL endpoints, context kits) that gives agents structured, scoped access without ever touching raw SQL
  
• A semantic enrichment layer (AI-generated descriptions, business glossary terms, golden queries, structural patterns learned from query history) that carries the meaning and intent agents need to interpret what they retrieve.
  
• A metadata foundation (schemas, lineage, usage patterns, quality signals) that serves as the single source of truth for what exists and how it connects.
  
• And underneath it all, the data sources themselves: Warehouses like Snowflake and BigQuery, BI tools like Looker and Tableau.
  

The shape of the stack matters because it shows how context flows from the data sources up to the agents that consume it, and it makes the dependency direction obvious: Everything above the metadata foundation inherits its quality. A bad foundation caps the ceiling on the rest of the stack, no matter how good the model or the prompting is.

What does the context layer actually have to do?

Five things, and they’re all non-negotiable. It has to:

• Unify structured metadata with unstructured organizational knowledge in one graph. Schema, lineage, ownership, and quality metrics on one side. Runbooks, FAQs, policies, and decision logs on the other. Both queryable as first-class nodes.
  
• Be semantically searchable, not just keyword-searchable. An agent asking about “organic engagement by market” needs to find a table originally described as “non-promoted pin interaction rates by country.” Lexical overlap won’t get you there. Embeddings and intent-based retrieval will.
  
• Expose itself through a protocol agents can consume natively. In practice today, that protocol is Model Context Protocol (MCP). Agents shouldn’t need bespoke integrations to read the graph. They should be able to point at an endpoint and start working.
  
• Be kept fresh and governed at scale, with quality and trust signals attached to every node. A graph that decays faster than it updates is worse than no graph, because it produces confidently wrong answers instead of obviously incomplete ones.
  
• Let agents enrich the graph, not just read it. Tagging, ownership assignment, description updates, glossary term proposals. Read-only context layers are static. Read-write context layers compound with use, which is the only model that scales as agent traffic grows.

How DataHub supports context-aware agents

As an enterprise context platform, DataHub provides the context layer described above as a working product.

• The Unified Context Graph connects technical metadata with Context Documents (runbooks, policies, FAQs, decision logs, etc.) and documentation from Notion and Confluence via external document connectors.
  
• Semantic Search makes the graph retrievable by intent. The DataHub MCP Server exposes it to any MCP-compatible agent (Claude, Cursor, Windsurf), with both read and write tools.
  
• The Agent Context Kit provides SDKs for LangChain, Google ADK, Vertex AI, Snowflake Cortex, and Copilot Studio so agents built in third-party frameworks inherit the same trust signals.
  

Together, these are what the layer looks like when it exists.

What this looks like in production: The Pinterest analytics agent

Pinterest’s analytics agent is the most-used internal tool at the company. Within two months of launch, it covered roughly 40% of the analyst population, with usage 10x the next most-used internal agent. It generates validated SQL from natural language, finds reusable queries, and handles the kind of complex tasks that used to require an analyst with deep warehouse knowledge, grounding its answers in the same governed tables and metric definitions the company’s analysts use every day. The numbers are public, and Pinterest’s engineering team published the architecture themselves.

The interesting part isn’t the model. It’s what’s underneath it.

Pinterest spent years getting their warehouse from roughly 400,000 tables down to about 100,000, then layered a tiering program on top that classified every remaining table by trust level:

• Tier 1 for cross-team production assets with strict documentation
  
• Tier 2 for team-owned tables with lighter standards
  
• Tier 3 for staging and legacy
  

They built a business glossary and propagated terms across more than 40% of their columns using join-based lineage, so a column documented in one table would inherit definitions across the tables it joined to. They reverse-mapped query history into semantic descriptions of analytical intent, turning years of accumulated SQL into a searchable library of “the questions analysts already know how to answer.” They cut manual documentation work by roughly 70% using AI-generated descriptions held in place by human review on the highest-tier assets.

DataHub is the system of record for all of it at Pinterest. Their engineering write-up names it explicitly and credits the governance work as having “laid the groundwork for everything that followed.” The agent is the visible layer, but the visible layer only works because of years of unglamorous infrastructure decisions made before “agent” was the word anyone was reaching for.

“This setup works because of a core insight: your analysts already wrote the perfect prompt.” – Pinterest Engineering

That line is the whole argument compressed. The intelligence isn’t in the model. It’s in the accumulated context the model is drawing from. The agent is only as smart as the graph beneath it, and the graph only gets smart through years of governance work that has nothing to do with AI and everything to do with treating metadata as a product.

The lesson isn’t “build like Pinterest.” Most teams won’t have a 100,000-table warehouse to govern, and the specific shape of Pinterest’s solution is theirs. The lesson is that context-awareness compounds from infrastructure decisions made years before the agent exists, and there’s no shortcut that gets you to a production-grade context-aware agent without doing the upstream work.

What to ask before you call an agent context-aware

If you’re building or evaluating an enterprise AI agent right now, the question to ask isn’t “how good is the model” or “how good is the prompting.” Both of those things matter, and both of them will be roughly equivalent across whatever you’re comparing.

The question is: What is this agent connected to, and is that thing complete, governed, and semantically coherent?

If the answer is “a vector index over a documentation dump,” the agent is context-connected. It will demo well. It will fail in production the first time someone asks a question whose answer depends on knowing which of three similarly named tables to trust.

If the answer is “a governed context graph with trust signals, business semantics, lineage, and a defined hierarchy for resolving conflicts,” the agent has a real shot at being context-aware. It will be slower to build, because the graph has to exist first. It will also be the only version that works at the scale and reliability an enterprise needs.

According to the IDC Value Study of DataHub Cloud (2026), organizations running on DataHub Cloud report 119% more AI/ML models reaching production compared to their prior baseline. The infrastructure investment shows up downstream in the metric that actually matters: How much of what you build ever leaves the lab.

The modern AI agents that will work in production aren’t the ones with the cleverest scaffolding. They’re the ones sitting on infrastructure that was built before anyone called it AI.

If you want to see what a working context layer looks like in practice, the DataHub live group demo walks through the architecture in more depth.

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