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An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pulled. The model did not fail. The context it was given did.

In the past six months, 57% of enterprises traced a confident but wrong AI agent answer to missing or inconsistent business context, and 31% said it happened more than once, according to a VB Pulse June 2026 survey of 101 qualified enterprises with more than 100 employees.

The reason is not hard to find. Retrieval over documents is the default way agents get business context for 38% of enterprises, nearly double the next closest approach. The way most enterprises choose a retrieval system compounds the problem. Ease of ingestion and operational simplicity lead the selection criteria, with retrieval accuracy running behind both. The accuracy problem only shows up after the system is already live.

There is a known fix for this, a governed context layer every agent reads from instead of guessing. Vendors are racing to roll out context platforms while most enterprises are still figuring out what it is.

75% don't have an agentic context layer yet

The context layer is meant to be a shared model of what business data actually means, built once and referenced consistently instead of re-derived by every agent that touches it.

The VentureBeat research shows the enterprise response to that idea is broad but unfinished. Twenty-five percent of respondents run one in production. Thirty-four percent are building one right now. The remaining 41% have not started.

Among companies already building or running a governed context layer, 78% report a confident-wrong failure — an AI agent that answered with total certainty and was still wrong. Among companies with no plans to build a layer, only 20% report the same thing. Companies that already got burned are far more likely to be building the fix. Companies that haven't been burned yet see no urgency.

What governed context looks like when someone actually builds one

Every major data and AI platform vendor is now building some version of this layer, and they are not converging on the same architecture.

DataHub is treating catalog metadata and years of analyst query behavior as a knowledge source, then keeping it current as a living system rather than a static wiki.

Microsoft's Fabric IQ is building a business ontology that any agent, not just Microsoft's own, can query over MCP.

Couchbase is pushing agent memory and context retrieval down to the edge, arguing the operational database is a more natural home for it than a search or analytics layer bolted on after the fact.

Pinecone's Nexus is compiling structural logic into the metadata layer ahead of runtime, betting that agents need pre-built structure more than they need faster search.

Snowflake runs a two-layer system, Horizon Context for customer-managed definitions and Cortex Sense for context the platform infers on its own.

Oracle's Unified Memory Core takes the opposite approach, folding vector, graph and relational data into one transactional engine so there is no sync layer left to go stale.

Google's Knowledge Catalog mines query logs and usage patterns to curate semantic context automatically.

AWS's Context service makes the same bet, a knowledge graph that gets smarter from how agents actually use it rather than from manual re-curation.

Analysts converge on one diagnosis

The vendor approaches differ. What analysts and practitioners have told VentureBeat about the underlying problem, across a run of interviews this year, does not.

When DataHub's context layer push landed this spring, Constellation Research VP and principal analyst Michael Ni framed the stakes in blunt terms. "Whoever controls runtime context controls the AI decision layer for enterprise data," Ni said. He was equally direct about how far any single product actually gets a buyer. "Vector memory isn't business meaning, business meaning isn't governance and governance isn't execution," Ni said.

In the same interview, BARC analyst Kevin Petrie pointed to a narrower but concrete gap. Most context platforms concentrate on structured tables, he said, which give agents trusted facts but miss the harder, messier context locked in documents and unstructured content, exactly the material a business actually runs on day to day.

Stephanie Walter, practice leader for AI Stack at HyperFRAME Research, made a related point earlier this year when VentureBeat asked her about enterprise context fragmentation .

"The market is converging on the same conclusion," Walter said. "Agents don't just need more tokens or better models. They need governed, current, low-latency context." She made a similar case in an earlier review of Pinecone's Nexus launch , careful not to overstate how new any of this is. Nexus, she said, "shifts knowledge work from runtime chaos to pre-compiled structure. But it's an evolution of RAG architecture, not a complete reinvention."

Gartner's Arun Chandrasekaran, reviewing the same launch, offered the more forward-looking read. Agentic AI, he said, is moving from pure information retrieval toward a reasoning architecture, one where long context works as short-term memory and a vector database functions as deep storage underneath it.

The fragmentation problem shows up hardest at the practitioner level, where separate tools for retrieval, memory and access control were never built to agree with each other. Steven Dickens, CEO and principal analyst at HyperFRAME Research, put it bluntly after Oracle's AI database push landed this spring. "Data teams are exhausted by fragmentation fatigue," Dickens said. "Managing a separate vector store, graph database and relational system just to power one agent is a DevOps nightmare."

Matt Kimball at Moor Insights and Strategy, in that same story, put the production reality more simply. Getting an agent working is not the hard part, he said. The struggle is running it in production, where the goal becomes removing the distance between data and execution rather than adding another layer on top of it.

What this means for enterprises

Here's what this adds up to for enterprises building on this layer.

Retrieval alone will not close the context gap. RAG is the default source for context in most enterprises today, and it is also the layer most closely associated with the confident-wrong-answer failure. Adding more documents or a bigger index does not fix a definition that is inconsistent across systems.

The semantic context layer is where the budget is actually moving, even where it hasn't shipped. Fifty-eight percent of enterprises are already engaged — building or in production — but only 25% have actually gotten a layer live. That gap shows where enterprises have decided to spend, not where they've arrived.

No single vendor owns the architecture yet, and that is likely to stay true for a while. Enterprises evaluating this layer should expect to integrate rather than pick a single winner, at least for the next several quarters.

The buying decision is happening this year, and it is concentrated among the companies already burned by it. Fifty-seven percent of enterprises plan to switch or add a retrieval or context platform within the next twelve months. That intent is not spread evenly. Enterprises that reported a repeat confident-wrong failure plan to switch or add a provider at roughly 81%, against 32% among enterprises that never hit the problem. The companies shopping for new context tooling right now are largely the ones whose agents already got it wrong.

The agents are already running. The context underneath most of them is still being built, and the vendor selling the fix is being chosen this year.

This data will be part of a broader conversation at VB Transform 2026 on July 14 and 15 in Menlo Park: the context gap enterprises are racing to close, and which of the emerging approaches — governed semantic layers, hybrid retrieval, provider-native bundles — actually holds up in production.

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Credit: Google

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Google quietly launched Magic Pointer on the Play Store ahead of the Googlebook, offering an early preview of its AI-powered desktop experience.

Magic Pointer turns anything you select on screen into a Gemini prompt, surfacing contextual AI actions instead of acting as a standalone app.

Google’s demo shows practical AI shortcuts, including Search with Lens, AI image generation via Nano Banana, and shopping suggestions.

Google is unbundling features of its upcoming hardware even before the hardware is on the market. While we wait for the much-hyped Googlebook to land this fall, the company has quietly rolled out an app called Magic Pointer to the Google Play Store, giving us our first real look at how Google intends to integrate Gemini into its new desktop environment.

The Play Store description is straightforward: “Select anything on your screen to get contextual AI suggestions and seamlessly get help from Gemini.” The feature seems to be built around a cursor with Gemini’s signature sparkle icon rather than an app or assistant in itself, letting you turn anything you select into an AI-powered starting point. Google also previously confirmed the Magic Pointer experience for Gemini in Chrome .

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Xiaomi’s N90 launches a new sub-brand called Sky Nomad.

A drop-in table turns the cabin into a rolling meeting space.

This is the brand’s first range-extender after two pure EVs.

We’ve known for over 18 months that Chinese consumer tech giant and automaker Xiaomi has been developing a full-size SUV to sit at the top of its range. This vehicle has now been previewed for the first time, wearing a new badge and aiming to double as an apartment on wheels.

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Read: Xiaomi May Be Building A 2,000+ HP Coupe To Chase Porsche

New images that surfaced through a filing on China’s Ministry of Industry and Information Technology website list both a standard model and a dedicated camping version.

Badged the N90, the SUV kicks off a new Xiaomi series called Sky Nomad. We’re not convinced it needed the distinctive nameplate, but there’s no arguing it looks radically different from the brand’s other cars, the SU7 sedan and YU7 SUV . It’s also the company’s first vehicle built as a range-extender rather than a BEV.

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It rides on Xiaomi’s Kunlun architecture, with the N90 measuring 208.1 inches (5,285 mm) long, 78.7 inches (1,998 mm) wide, and 71.9 inches (1,825 mm) tall, sitting on a 121.3 inch (3,080 mm) wheelbase that puts it in the same class as other full-size SUVs including the Li Auto 9, Auto M9, and Zeekr 9X . The camping variant stands higher at 75.8 inches (1,925 mm), the extra height making room for a retractable roof and a sleeping platform up top.

Early photos of the SUV show it lacks any obvious design ties to existing Xiaomi models, adopting a boxier shape to maximize interior space, with roof-mounted LiDAR hardware enabling advanced driver-assistance functions.

A Cabin For Any Scenario

Speaking of the interior, that’s where most of the action seems to be. Schematics of the cabin show both front seats swiveling 180 degrees to face the rear, with a free-standing table that drops in between them, turning the interior into something close to a meeting room. The second row folds flat alongside the third, opening up enough room for a generous bed.

A batch of GIFs previewing the cabin fills in the rest, showing a well-appointed interior with plush materials, leather throughout, and the expected large central infotainment display. Buyers choose between five- and seven-seat layouts, with zero-gravity chairs standard in the second row and a movable ceiling-mounted screen overhead.

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The N90 will launch later this year with a 1.5-liter turbocharged range-extender engine rated at 112 kW (150 hp), feeding a dual-motor setup that peaks at 210 kW (282 hp) and 100 kW (134 hp). Xiaomi quotes a top speed of 190 km/h (118 mph). Backing the system is a 76 kWh battery good for a WLTC electric range of 363 to 370 km (226 to 230 miles).

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Christopher Nolan says movies like Backrooms and Obsession are proof positive that younger audiences will sit through a three-hour Greek epic like The Odyssey – despite star Matt Damon's doubts.

"I think I know what he was driving at, because it does seem like a long time since somebody made a film like this in this type of way, where you travel the world, get together a cast of thousands, and so on," Nolan told the Telegraph when asked about Damon's thoughts. The actor previously stated that he "knew that this was the last chance I was going to have to do something like this."

"But there’s a defeatist aspect of viewing it that way that I don’t agree with. I think cinema is vital and essential and continues to transform itself," Nolan continues. "We’ve got all these great new young voices in movies, making the medium their own and moving it forward."

The filmmaker cited Curry Barker and Kane Parsons as examples of the landscape changing for the better, as mega-hits Backrooms and Obsession are concrete evidence of young people going to the theater to see weird stuff. As of now, Backrooms has pulled in $360 million worldwide against a budget of $10 million, with Obsession earning a whopping $406 million against a budget of under $1 million.

Nolan adds: "This is why I never bought into the arguments that young audiences' attention spans are too fried to enjoy a three-hour Greek epic. Those films are so mysterious and ruminative. I mean, parts of Backrooms are like David Lynch at his most obscure. And yet young people can’t get enough of them."

The Odyssey is based on Homer's epic of the same name and stars Damon as Odysseus, the Greek king of Ithaca, as he tries to find his way back home after the Trojan War. It's got a runtime of 2 hours and 52 minutes.

The Odyssey arrives this July 17. In the meantime, check out our guide to all the upcoming movies in 2026 and beyond, or, check out our list of movie release dates.