Ciente > Blog > News

WPP Unveils New Suite of AI Agents to Equip Clients with Better Outcomes

Category: News

Discover the latest in technology, from breaking news to trending updates. Ciente.io brings together all key tech stories.

WPP Unveils New Suite of AI Agents to Equip Clients with Better Outcomes
News

WPP Unveils New Suite of AI Agents to Equip Clients with Better Outcomes

WPP Unveils New Suite of AI Agents to Equip Clients with Better Outcomes

WPP is amping up its business strategy. As 2026 kicks off, a new suite of AI agents is its first move.

WPP just introduced Agent Hub on its AI platform WPP Open.

But it’s not another watered-down tech demo. It’s an internal app store for agentic AI built on 150+ ready-made agents powered by WPP’s own decades of data, strategy, and creative muscle.

It isn’t fringe hype. It’s the holding company slamming its “collective intelligence” into a product clients can actually use.

Call it what it is: packaged know-how.

The brand analytics agent taps approximately 30 years of proprietary brand equity data. Behavioural science and analogies agents take frameworks that live in human brains. And now they live in AI logic.

Creative brain is basically WPP’s century-plus of creative instinct in software.

The messaging is classic WPP spin with a purpose: “human brilliance, amplified by AI.” They frame it as democratising expertise- no silos, no single guru blocking access. Clients and teams receive these agents instantly, with validation gates and compliance checks to keep outputs trustworthy.

Here’s the real deal: this isn’t about replacing strategists or creatives. It’s about scaling the most intelligent thinking inside WPP across every brief at lightning speed. It’s a defensive play and an offense.

Agencies lose excuses (“We don’t have enough brainpower/data”), and clients get smarter results faster, assuming the agents truly deliver consistently.

But let’s be blunt: agentic AI only matters if it makes work measurably smarter and not just faster. WPP’s pitch is solid: clients get access to deep expertise via software, not only people.

Now the ball’s in the clients’ court-

Will this actually shift outcomes or merely add another layer of tech marketing?

GeeLark Innovates Social Media Marketing with Its Social Media Automation Tool
News

GeeLark Innovates Social Media Marketing with Its Social Media Automation Tool

GeeLark Innovates Social Media Marketing with Its Social Media Automation Tool

GeeLark is betting that social media automation needs to look human again.

Social media automation has been lying to marketers for years. Scheduling posts was never a strategy. It was convenience dressed up as control. As platforms went mobile-first and behavior-obsessed, most tools stayed stuck in dashboards and APIs.

GeeLark breaks from that playbook.

Instead of automating around social platforms, GeeLark automates inside them. Its cloud-based Android phones behave like real devices- opening apps, scrolling feeds, posting content, and engaging with other accounts. No browser tricks. No brittle API dependencies. Just native app behavior, at scale.

That distinction matters more than most marketers realize. Algorithms don’t reward schedules. They reward patterns. GeeLark’s approach aligns automation with how platforms actually interpret legitimacy today- not how automation vendors prefer to explain it.

It isn’t a silver bullet. Tools that mimic human behavior always operate near a fault line. Platforms are increasingly sensitive to anything that looks manufactured, no matter how “real” it appears. Used recklessly, the scale still attracts scrutiny.

But GeeLark deserves credit for pushing automation out of its comfort zone. It’s not selling efficiency. It’s about marketing relevance in a landscape that punishes anything that feels mechanical.

Whether marketers use that power with restraint is the real test. Not the technology itself.

A New AI Milestone or Yet Another Stint? Data Center Investments Reach $61 bn in 2025
News

A New AI Milestone or Yet Another Stint? Data Center Investments Reach $61 bn in 2025

A New AI Milestone or Yet Another Stint? Data Center Investments Reach $61 bn in 2025

As Open AI floats through uncharted territory, could the $61 bn data center market actually reach profitability as promised?

ChatGPT now lets you adjust your email’s warmth levels. Alphabet acquired a new data center company. “The AI bubble is about to burst,” economists warn. Google announces new Gemini Flash 3 for speed. Everyone’s losing money on AI.

These are some of today’s headlines on AI. And they aren’t all enthusiastic. The response to AI has suddenly become quite diverse. And largely disappointing. It’s as if a veil has been removed, and the public perceives AI as more of the same high-level tech that’s supposed to cater to the chosen few.

Beyond this curtain? AI’s significance is dismissive.

However, that and countless warnings from economists haven’t stopped the AI enthusiasts. As the echo of the AI bubble burst makes the rounds every other day, another company ends up investing a few billion dollars in related infrastructure and hardware.

The disconnect is apparent.

The global data center market reached $61 billion this year. First, it was the chip frenzy that sent NVIDIA’s worth skyrocketing. And now, it’s the construction frenzy. The insatiable demand for AI isn’t nearly as evident as the demand for hardware, real estate, and energy. The nitty-gritty.

As an increasing number of data centers pop up, the market is questioning the returns. According to HBR, there are high variable spending, but low variable returns when it comes to AI.

The money movement is also apparent as all the tech and AI powerhouses hold hands to accelerate their AI roadmaps. It’s a well-thought-out strategy. But the returns are the real facet in question.

There’s not much to show.

Last week, the Wall Street Journal published a report on Notion. Its AI helps generate content, search, take down meeting notes, and research. It ate into 10% of Notion’s profit margin. And truly, it’s the actions that any user can carry out within meetings.

AI was equated with efficiency and cheaper labor costs. But it’s adding on- more than ever. Unproven returns. But enthusiastic overspending.

OpenAI will burn through approximately. $150 billion between 2024 and 2029, according to analysts. But it’s only in 2029 that the AI powerhouse could potentially turn a profit. Then it will have something to show for all its investments. To justify all the billions.

The global AI bubble may or may not pop, but investors and analysts have noticed a pattern-

The money movement is circular, and the entire US economy rests on that.

Google News Launches Innovative Audio Briefings with a New Listen Tab
News

Google News Launches Innovative Audio Briefings with a New Listen Tab

Google News Launches Innovative Audio Briefings with a New Listen Tab

Google News adds an AI-powered Listen tab with audio briefings for hands-free updates, clear source links, playback controls, and region-limited rollout.

Google is no longer asking you to read the news.

With its new audio briefings feature, Google News is stepping into podcast territory. Quietly. Intentionally. And with more care than most AI news experiments so far.

The update introduces a Listen tab on Android. You will get short, AI-generated briefings you can do anything- play, pause, rewind, skip, or speed up. It’s not meant to be a robotic readout of headlines. It feels closer to a daily news digest, minus the host banter.

The significant detail is attribution. Every audio briefing links back to the original articles. Sources are visible. Stories aren’t dissolved into a single AI soup. Google is clearly trying to avoid the highest form of criticism of AI summaries: stripping publishers of traffic and context.

It matters.

Audio is not a novelty anymore. People already listen to the news while doing chores. Until now, Google has significantly pushed users outward- toward podcasts or Assistant briefings. This feature pulls them inward. News stays inside the Google News ecosystem, but publishers still get credit and potential clicks.

That balance is deliberate.

There are limits, though. The rollout is restricted, mainly to the US. Other users may only see it after switching their region settings. Google has not committed to a global timeline. That hesitation suggests testing, not confidence.

The feature also avoids personalization hype. These briefings are topical, not deeply tailored. No grand claims about knowing what you want before you ask. That restraint is refreshing. It keeps expectations grounded and reduces the risk of algorithmic overreach.

From a strategy lens, this is Google defending attention. Text feeds are crowded. Video is expensive. Audio is efficient. It fits into dead time and keeps users engaged without demanding all the focus.

Still, the real test is durability. If this turns into another half-promoted experiment, it will fade. If Google invests in consistency, regional expansion, and publisher trust, the Listen tab could become a daily habit.

This is not Google reinventing news. It is Google adjusting the format. And sometimes, that is the intelligent move.

Google Wants Its Users to Wake Up With AI- A Morning Briefing by Gemini.
News

Google Wants Its Users to Wake Up With AI- A Morning Briefing by Gemini.

Google Wants Its Users to Wake Up With AI- A Morning Briefing by Gemini.

Google’s Gemini-powered CC emails you a tailored morning briefing from Gmail and Calendar to replace mindless scrolling with actionable insights.

Google just rolled out CC, a new AI agent built on its Gemini family of models, and it’s not another chatbot to ask trivia.

It’s designed to be the first thing you see in your inbox each day- a personalized “Your Day Ahead” briefing compiled from your Gmail, Calendar, Drive, and other signals. That’s an intelligent pivot for professionals tired of endless morning scrolling. Surfacing tasks, meetings, bills, and even drafting replies before your morning coffee.

What’s notable is how Google chose email as the primary interface rather than a standalone app. That decision keeps CC in your workflow, not off in a separate AI silo. You receive a daily digest straight to your inbox, and you can teach CC about preferences by replying to its emails or feeding it details it should remember.

It’s subtle, but that’s the point- this isn’t an AI you “use;” it lives inside the tools you already depend on.

But this launch isn’t without questions.

Google’s strategy of embedding AI into every corner of its products is relentless.

But there’s a hiccup. Privacy and control remain central concerns. Letting an AI sift through your inbox and documents for pattern recognition is powerful. But it still raises expectations about transparency and safeguards.

How much visibility will users have into what CC stores or forgets? How granular will the settings be?

Early access is limited to paid subscribers in the U.S. and Canada, hinting a cautious and iterative rollout.

In the larger AI arms race, CC isn’t flash; it’s tactical. It moves Gemini from a reactive assistant to a proactive partner in daily productivity. If executed well, this could recalibrate how we start our workdays, turning passive scrolling into purposeful action.

But as true with AI assistants, the promise depends on execution, not hype.

NVIDIA Unveils An Entire Family of Open Models: The Nemotron 3
News

NVIDIA Unveils An Entire Family of Open Models: The Nemotron 3

NVIDIA Unveils An Entire Family of Open Models: The Nemotron 3

NVIDIA doubles down on becoming a major model maker. Plans to increase investments in open-source tech.

The market’s beloved chip designer, NVIDIA, just unveiled a family of open-source models called the Nemotron 3.

It has made fortunes supplying chips to the market giants. But now it’s vamping its roadmap. NVIDIA is trying to expand its offerings, especially given that some market leaders have now begun designing and manufacturing their own capable-enough chips. Be it Anthropic, Google, or OpenAI.

That’s crucial for NVIDIA. But it has already found a roundabout- the family of open-source models- Nano (30 billion parameters), Super (100 billion parameters), and Ultra (500 billion parameters).

Open-source AI models are extremely substantial to AI research and development. That’s what most companies experiment with, prototype, and build upon. Right now, Chinese counterparts enjoy the dominance. Because even though Google and OpenAI also offer smaller models, they aren’t updated and refined as regularly.

But with Nemotron 3, NVIDIA might become the best of the best.

According to the company’s press release ahead of the launch, NVIDIA published specific benchmark scores. These scores showcase that these models are very easily downloadable and modifiable. And they run on one’s own hardware.

“Open innovation is the foundation of AI progress,” asserts Jensen Huang.

And with the Nemotron 3, NVIDIA plans to transform advanced AI. And offer developers the toolkit to efficiently and seamlessly develop scalable agentic AI systems. That remains the roadmap for now. To empower engineers and developers with transparency and efficiency.

And to further differentiate itself from its US rivals, NVIDIA is being quite flexible and transparent with the data used to train Nemotron. Because it’s not just a glimpse into user privacy and ethical practices, but opens up a segueway for developers to modify the model easily. Something that NVIDIA’s competitors moved away from in the past year due to fear of their research being stolen.

Additionally, the company is also launching tools for fine-tuning and customization, along with a new hybrid latent mixture-of-experts model architecture and libraries.

The only hindrance for NVIDIA? Its silicon has become a bargaining chip. It’s substantial to the AI and global economy. And this could work against the company as we witness intensifying competition in this sector.