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Enterprise AI has an agent deployment problem — most so-called agents are still chatbot wrappers

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enterprise AI agents

Enterprises are building the plane before they have a fleet

A new wave of VentureBeat Pulse Research, based on a June 2026 survey of 101 enterprise organizations (100+ employees), reveals a stark tension: companies are racing to build sophisticated orchestration layers for AI agents, but the vast majority of those so-called agents are still glorified chatbots.

The headline finding is blunt. When asked to honestly assess their own portfolios, 71% of respondents said a quarter or fewer of their deployed AI agents are true multi-step orchestrated workflows. Only 10% have crossed the halfway mark. The rest are single-prompt chatbot wrappers — dressed up in agent clothing, but doing nothing an orchestration layer is actually for.

This gap between ambition and reality is the central story of the research. Enterprises are standardizing on model-provider platforms, pouring money into workflow tooling, and designing hybrid control planes — all before most of their agents can execute a multi-step task reliably.

Anthropic’s Claude dominates the platform race

When it comes to which platform enterprises are betting on, one name stands out. Anthropic’s Claude is the primary orchestration platform for 40% of respondents — more than double the next contender. Microsoft sits at 18%, OpenAI at 13%, and Google and Amazon trail in single digits. Open-source frameworks like LangChain and LangGraph, which dominate technical discussions, barely register in enterprise deployment.

The logic behind the choice is what the researchers call “model gravity.” The single biggest factor driving platform selection — cited by 21% of respondents — is native alignment with a state-of-the-art base model. Enterprises are picking the orchestration environment that comes with the frontier model they already want to build on.

But satisfaction is lukewarm. Respondents rated their platforms at 3.94 out of 5 overall, with “ease of implementation” the weakest score at 3.85. And 96% plan to change their orchestration approach within the year. These are tools enterprises tolerate, not love.

Reliability rules — but most agents can’t deliver it

What do enterprises actually want from orchestration? The answer is boring but brutal: reliability. Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of primary success metrics. Developer productivity and end-user experience lag far behind.

This makes the chatbot trap even more pointed. Enterprises define success as dependable multi-step execution, yet most of their deployed agents can’t do multi-step work at all. The ambition is real; the portfolio is not.

The trap is unevenly distributed. Among smaller enterprises (under 2,500 employees), 77% say a quarter or fewer of their agents do true multi-step work. For larger organizations, that figure drops to 62% — still high, but meaningfully better. The chatbot trap is, directionally, a mid-market condition.

Hybrid control planes: The hedge against lock-in

Enterprises are designing their control architecture with one fear in mind: vendor lock-in. By the end of 2026, 51% expect a hybrid control plane — part provider-native, part external. Only 6% plan to hand control entirely to a provider-managed service.

The reason is clear. When asked what worries them most about letting control live inside a model provider, 35% said vendor lock-in, up from 24% in an earlier April-May wave. Security and permissioning limitations (28%) and inflexibility across models (21%) round out the concerns.

This is a notable shift. In the earlier survey, security was the top concern. By June, lock-in had taken the lead. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

The hybrid control plane is the architectural hedge. Enterprises will build on a provider’s platform, but they will not be governed entirely by it.

Investment flows to tooling, but cost control lags

Where is the money going? Agent workflow tooling leads spending plans at 34%, followed by security and permissions enforcement at 25%, and scaling infrastructure at 20%. Monitoring and debugging draws a smaller 11%.

The weight on tooling and permissions over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

But fiscal control over token consumption remains reactive. More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop a runaway agent before the bill arrives — they learn of it from the logs afterward. Another 32% rely entirely on native caps built into their platform, a control only as good as the provider’s tooling.

Only the enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are treating token burn as an engineering problem to be controlled deterministically.

Again, size matters. About one in three smaller enterprises (34%) exercises only reactive control of agent spend, against 20% of larger ones. The mid-market is running the least mature agents on the least instrumented budgets.

The bottom line: The layer is real; most of the agents aren’t yet

This wave of research paints a clear directional picture. Enterprises have decided how they want to orchestrate agents — on model-provider platforms, with hybrid control planes, judged by reliable multi-step execution. The platforms, budgets, and strategies are being put in place.

But the deployed reality is thin. Seventy-one percent of enterprises admit a quarter or fewer of their agents are genuinely orchestrated. Only 10% are past the halfway mark. And more than a quarter cannot stop a runaway agent in real time.

The orchestration layer is being built ahead of the orchestrated portfolio it is meant to run. That is not necessarily a contradiction — it may be a roadmap. The question for subsequent waves is whether the deployed reality closes the gap on the ambition, or whether the chatbot trap proves stickier than the roadmap assumes.

For organizations serious about AI agent deployment, the takeaway is sobering: invest in the orchestration layer by all means, but be honest about what your agents can actually do. And if you can’t stop a runaway agent in real time, fix that before you let it run unsupervised.

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Google’s Gemini Notebook Now Lets You Cite Books You Own — Here’s How It Works

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Google’s Notebook AI Just Got Smarter — With Your Bookshelf

If you’ve ever wished your AI assistant could quote directly from a cookbook you own, or pull a specific argument from a biography sitting on your digital shelf, here’s the news you’ve been waiting for.

Google is rolling out a new capability for Gemini Notebook (the tool formerly known as NotebookLM) called Expert Intelligence. The name sounds fancy, but the idea is simple: instead of only drawing from documents you upload or pages you link, the AI can now tap into digital books you’ve actually purchased from the Google Play Books library.

That’s right — your personal library becomes a source. No more copy-pasting excerpts or hoping the AI finds a relevant PDF. If you own the book, you can cite it directly.

How Expert Intelligence Works in Practice

Let’s say you’re planning a week of dinners built entirely around chicken and spinach. Instead of scouring the web, you can ask Gemini to pull recipes from a Martha Stewart cookbook you own. The AI will search the text, pull relevant passages, and cite them as sources.

But it doesn’t stop at recipes. You can turn any owned book into a variety of formats:

  • Infographics — visual summaries of key concepts from the book.
  • Audio Overviews — listen to a conversational recap of the material.
  • Quizzes — test your understanding based on the book’s content.

It’s a natural extension of what NotebookLM already does with uploaded sources, just with a much more curated pool of knowledge.

Not a Free-for-All: Sharing Has Limits

Here’s a catch that’s worth knowing before you get too excited. If you create a shared notebook and add a book as a source, your collaborators can’t just freeload off your purchase. They’ll need to own or buy their own copy of the book to fully use Expert Intelligence. Google’s being careful about copyright, and that’s probably wise.

Which Publishers Are On Board?

The feature launches with a solid lineup of major publishers. We’re talking Bloomsbury, De Gruyter Brill, Johns Hopkins University Press, Macmillan Publishers, O’Reilly Media, and Penguin Random House. All told, that’s over 100,000 titles available from day one.

That’s a big deal for students, researchers, and lifelong learners who already rely on NotebookLM for organizing their research.

Free Book Offer — But Act Fast

To kick things off, Google is giving away one free book to users in the US. The catch? It’s “while supplies last,” so you’ll want to check the app sooner rather than later if you’re interested.

It’s a smart marketing move, honestly. Get people hooked on the feature with a freebie, and they’ll likely buy more books to keep using it.

What About the Main Gemini App and Search?

Right now, Expert Intelligence is available only in the Gemini Notebook app and its web dashboard. But Google has confirmed it’s planning to bring the feature to the core Gemini app and AI mode in Search down the road.

That expansion could be huge. Imagine asking Gemini in Search to explain a concept “according to this book” and getting a cited answer pulled straight from the text. It’s a glimpse of where AI-assisted research is heading.

Featured Notebooks: Extra Insights from Authors

Beyond the book-citing feature, Google has partnered with a handful of authors to create Featured Notebooks. These offer additional insights that go beyond what’s in the book itself — think of them as bonus material, but integrated right into your research workflow.

It’s a nice touch that adds value for readers who want more context or behind-the-scenes thinking from the authors they admire.

Why This Matters for Your Research Workflow

If you’re already using NotebookLM for projects, this is a meaningful upgrade. You no longer have to juggle between your book library and your notes app. The books you own become part of your AI-powered research stack.

That said, it’s not a replacement for critical thinking. The AI is still pulling from what it finds, and you should always verify the context. But as a starting point for essays, reports, or even just personal learning, it’s a powerful tool.

For more on how to get the most out of Google’s AI tools, check out our guide on using NotebookLM for research and our rundown of Google AI features for productivity.

So, is Expert Intelligence worth trying? If you own books on Google Play Books and you’re already in the Notebook ecosystem, absolutely. Just remember to check the free book offer before it runs out.

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Google Dreambeans AI app: Your personalized daily feed is now free — here’s how it works

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Dreambeans AI app

Google just made Dreambeans free — what changed?

Google has quietly dropped the paywall on Dreambeans, the Dreambeans AI app that curates a personalized daily feed of stories just for you. Starting now, any Google Account holder in the US can try it without spending a dime. The news first surfaced via 9to5Google, and it’s a significant shift from the app’s earlier rollout.

Dreambeans first appeared in June, but only for Google AI Ultra subscribers. Later, it expanded to AI Pro users. Now? It’s open to everyone. That’s a big deal for anyone curious about AI-driven content discovery but not ready to pay for a subscription.

Setup isn’t instant, though. Google says it takes about a day for the app to generate your first set of stories. The app itself is available on both Android and iOS, so you can start the process on your phone and check back tomorrow morning.

How the Dreambeans AI app builds your daily feed

Think of Dreambeans as a hyper-personal version of Google Discover, but smarter. With your permission, it taps into several Google services to understand what you care about. Here’s the breakdown:

  • Gmail and Workspace — Provides real-world context like receipts, bookings, or an upcoming flight.
  • Google Photos — Picks up on the people and places you frequently capture.
  • Calendar — Notes events and appointments that might spark story ideas.
  • YouTube — Tracks your active hobbies and viewing habits.
  • Search history — Flags interests you’re just beginning to explore.

Every morning, the app stitches these data points into a fresh batch of story suggestions. That could be a hike worth trying, a new restaurant in your neighborhood, or an event happening nearby. It’s not just generic content — it’s tailored to your life.

Custom artwork, not stock photos

One of the coolest touches? Each story comes with custom artwork generated by the Nano Banana image generator. Instead of boring stock photos, you get illustrated scenes that often depict you and people you know. It adds a personal, almost whimsical feel to the feed.

More Google Labs experiments worth trying

Dreambeans isn’t the only experiment coming out of Google Labs lately. If you run a small business, Pomelli can now build your entire brand identity from scratch — from your color palette to a full working website. That’s a serious time-saver for entrepreneurs who don’t have design skills.

For music lovers, ProducerAI lets you describe a song idea and walk away with actual beats, album art, and even a music video to match. It’s a wild tool for hobbyists and creators alike.

If you’re a student, there’s another perk worth noting. Google is offering a free year of Gemini AI Pro through a new student hub. That’s a smart move if you want to test premium AI features before committing to a subscription elsewhere.

Is Dreambeans the next big thing in AI feeds?

Honestly, it’s too early to say. But the move to make it free suggests Google is serious about gathering user feedback and refining the product. The AI feed space is getting crowded, with competitors like Microsoft and various startups pushing their own personalized content engines.

What sets Dreambeans apart is the depth of integration. It’s not just scraping public data — it’s reading your inbox and your photo library. That’s powerful, but it also raises privacy questions. You’ll need to weigh the convenience against the data access.

For now, if you’re in the US and curious, it’s worth giving it a shot. The setup takes a day, but the payoff is a feed that actually feels like it knows you.

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Sony Music, Warner Chappell sue Anthropic, accusing AI lab of ‘brazen’ copyright theft

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Sony Music Warner Anthropic lawsuit

Publishers take Anthropic to court over training data

Sony Music Publishing, Warner Chappell, and a coalition of other music publishers have filed a lawsuit against Anthropic and its co-founders, Dario Amodei and Benjamin Mann. The complaint, lodged late Friday in the U.S. District Court for the Northern District of California, accuses the AI lab of running a “brazen campaign of illegally torrenting, scraping, and downloading copyrighted works.”

This is more than a routine licensing dispute. The publishers are alleging what they call “blatant theft” — that Anthropic used thousands of copyrighted songs, lyrics, and sheet music to train its Claude AI models without permission or payment.

Anthropic isn’t staying quiet. “We disagree with the publishers’ claims and we intend to defend ourselves robustly in court,” a spokesperson told TechCrunch in an emailed statement.

Not the first IP fight for Anthropic

This isn’t Anthropic’s first rodeo in court over intellectual property. Some of the same lawyers behind this case previously represented Concord Music Group and Universal Music Group in a January lawsuit. They also led the Bartz v. Anthropic case, where a group of authors accused the company of using copyrighted books to train Claude.

In the Bartz case, a judge ordered Anthropic to pay $1.5 billion. The ruling was nuanced: using copyrighted works to train AI was deemed legal, but obtaining that content through piracy was not. That distinction matters here.

What’s different this time?

The new lawsuit is notably broader than its predecessors. It accuses Anthropic of “flagrant piracy” through illegal torrenting to secure millions of copies of books — including works containing lyrics and sheet music. The publishers argue this wasn’t just a copyright gray area; it was outright theft on a massive scale.

Legal experts will likely debate whether this case breaks new ground or simply extends the arguments from Bartz. Either way, the music industry is clearly drawing a line in the sand.

Why the music industry is pushing back

For publishers, the stakes couldn’t be higher. If AI companies can freely train on copyrighted music without compensation, the value of their catalogs could plummet. Lyrics, after all, are the backbone of countless streaming services, karaoke apps, and print publications.

This lawsuit isn’t just about money. It’s about control — who gets to decide how creative works are used in the age of generative AI.

What happens next

Anthropic has vowed to fight the claims. The company’s defense will likely hinge on the same arguments that partially succeeded in Bartz: that training on copyrighted material is transformative and should be allowed under fair use.

But the piracy angle complicates things. Torrenting, even for AI training, carries a different legal weight than simply scraping publicly available data. If the publishers can prove Anthropic knowingly engaged in illegal downloads, the court may not be sympathetic.

This case is one to watch. It could set a precedent for how AI companies source their training data — and whether the music industry can demand a slice of the AI pie.

For more on how AI is reshaping creative industries, check out our piece on AI music generation copyright issues and how AI companies handle licensing disputes.

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