Imagine chatting with an AI about a health concern, and instead of just processing your words, it could actually look at the photo of that worrying rash or make sense of your ECG printout. That’s what Google is aiming for.

We already knew AMIE showed promise in text-based medical chats, thanks to earlier work published in Nature. But let’s face it, real medicine isn’t just about words.

Doctors rely heavily on what they can see – skin conditions, readings from machines, lab reports. As the Google team rightly points out, even simple instant messaging platforms “allow static multimodal information (e.g., images and documents) to enrich discussions.”

Text-only AI was missing a huge piece of the puzzle. The big question, as the researchers put it, was “Whether LLMs can conduct diagnostic clinical conversations that incorporate this more complex type of information.”

Google teaches AMIE to look and reason

Google’s engineers have beefed up AMIE using their Gemini 2.0 Flash model as the brains of the operation. They’ve combined this with what they call a “state-aware reasoning framework.” In plain English, this means the AI doesn’t just follow a script; it adapts its conversation based on what it’s learned so far and what it still needs to figure out.

It’s close to how a human clinician works: gathering clues, forming ideas about what might be wrong, and then asking for more specific information – including visual evidence – to narrow things down.

“This enables AMIE to request relevant multimodal artifacts when needed, interpret their findings accurately, integrate this information seamlessly into the ongoing dialogue, and use it to refine diagnoses,” Google explains.

Think of the conversation flowing through stages: first gathering the patient’s history, then moving towards diagnosis and management suggestions, and finally follow-up. The AI constantly assesses its own understanding, asking for that skin photo or lab result if it senses a gap in its knowledge.

To get this right without endless trial-and-error on real people, Google built a detailed simulation lab.

Google created lifelike patient cases, pulling realistic medical images and data from sources like the PTB-XL ECG database and the SCIN dermatology image set, adding plausible backstories using Gemini. Then, they let AMIE ‘chat’ with simulated patients within this setup and automatically check how well it performed on things like diagnostic accuracy and avoiding errors (or ‘hallucinations’).

The virtual OSCE: Google puts AMIE through its paces

The real test came in a setup designed to mirror how medical students are assessed: the Objective Structured Clinical Examination (OSCE).

Google ran a remote study involving 105 different medical scenarios. Real actors, trained to portray patients consistently, interacted either with the new multimodal AMIE or with actual human primary care physicians (PCPs). These chats happened through an interface where the ‘patient’ could upload images, just like you might in a modern messaging app.

Afterwards, specialist doctors (in dermatology, cardiology, and internal medicine) and the patient actors themselves reviewed the conversations.

The human doctors scored everything from how well history was taken, the accuracy of the diagnosis, the quality of the suggested management plan, right down to communication skills and empathy—and, of course, how well the AI interpreted the visual information.

Surprising results from the simulated clinic

Here’s where it gets really interesting. In this head-to-head comparison within the controlled study environment, Google found AMIE didn’t just hold its own—it often came out ahead.

The AI was rated as being better than the human PCPs at interpreting the multimodal data shared during the chats. It also scored higher on diagnostic accuracy, producing differential diagnosis lists (the ranked list of possible conditions) that specialists deemed more accurate and complete based on the case details.

Specialist doctors reviewing the transcripts tended to rate AMIE’s performance higher across most areas. They particularly noted “the quality of image interpretation and reasoning,” the thoroughness of its diagnostic workup, the soundness of its management plans, and its ability to flag when a situation needed urgent attention.

Perhaps one of the most surprising findings came from the patient actors: they often found the AI to be more empathetic and trustworthy than the human doctors in these text-based interactions.

And, on a critical safety note, the study found no statistically significant difference between how often AMIE made errors based on the images (hallucinated findings) compared to the human physicians.

Technology never stands still, so Google also ran some early tests swapping out the Gemini 2.0 Flash model for the newer Gemini 2.5 Flash.

Using their simulation framework, the results hinted at further gains, particularly in getting the diagnosis right (Top-3 Accuracy) and suggesting appropriate management plans.

While promising, the team is quick to add a dose of realism: these are just automated results, and “rigorous assessment through expert physician review is essential to confirm these performance benefits.”

Important reality checks

Google is commendably upfront about the limitations here. “This study explores a research-only system in an OSCE-style evaluation using patient actors, which substantially under-represents the complexity… of real-world care,” they state clearly. 

Simulated scenarios, however well-designed, aren’t the same as dealing with the unique complexities of real patients in a busy clinic. They also stress that the chat interface doesn’t capture the richness of a real video or in-person consultation.

So, what’s the next step? Moving carefully towards the real world. Google is already partnering with Beth Israel Deaconess Medical Center for a research study to see how AMIE performs in actual clinical settings with patient consent.

The researchers also acknowledge the need to eventually move beyond text and static images towards handling real-time video and audio—the kind of interaction common in telehealth today.

Giving AI the ability to ‘see’ and interpret the kind of visual evidence doctors use every day offers a glimpse of how AI might one day assist clinicians and patients. However, the path from these promising findings to a safe and reliable tool for everyday healthcare is still a long one that requires careful navigation.

(Photo by Alexander Sinn)

See also: Are AI chatbots really changing the world of work?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Starting with Integrations, the feature builds on a technical standard Anthropic released last year (the Model Context Protocol, or MCP), but makes it much easier to use. Before, setting this up was a bit technical and local. Now, developers can build secure bridges allowing Claude to connect safely with apps over the web or on your desktop.

For end-users of Claude, this means you can now hook it up to a growing list of popular work software. Right out of the gate, they’ve included support for ten big names: Atlassian’s Jira and Confluence (hello, project managers and dev teams!), the automation powerhouse Zapier, Cloudflare, customer comms tool Intercom, plus Asana, Square, Sentry, PayPal, Linear, and Plaid. Stripe and GitLab are joining the party soon.

So, what’s the big deal? The real advantage here is context. When Claude can see your project history in Jira, read your team’s knowledge base in Confluence, or check task updates in Asana, it stops guessing and starts understanding what you’re working on.

“When you connect your tools to Claude, it gains deep context about your work—understanding project histories, task statuses, and organisational knowledge—and can take actions across every surface,” explains Anthropic.

They add, “Claude becomes a more informed collaborator, helping you execute complex projects in one place with expert assistance at every step.”

Let’s look at what this means in practice. Connect Zapier, and you suddenly give Claude the keys to thousands of apps linked by Zapier’s workflows. You could just ask Claude, conversationally, to trigger a complex sequence – maybe grab the latest sales numbers from HubSpot, check your calendar, and whip up some meeting notes, all without you lifting a finger in those apps.

For teams using Atlassian’s Jira and Confluence, Claude could become a serious helper. Think drafting product specs, summarising long Confluence documents so you don’t have to wade through them, or even creating batches of linked Jira tickets at once. It might even spot potential roadblocks by analysing project data.

And if you use Intercom for customer chats, this integration could be a game-changer. Intercom’s own AI assistant, Fin, can now work with Claude to do things like automatically create a bug report in Linear if a customer flags an issue. You could also ask Claude to sift through your Intercom chat history to spot patterns, help debug tricky problems, or summarise what customers are saying – making the whole journey from feedback to fix much smoother.

Anthropic is also making it easier for developers to build even more of these connections. They reckon that using their tools (or platforms like Cloudflare that handle the tricky bits like security and setup), developers can whip up a custom Integration with Claude in about half an hour. This could mean connecting Claude to your company’s unique internal systems or specialised industry software.

Beyond tool integrations, Claude gets a serious research upgrade

Alongside these new connections, Anthropic has given Claude’s Research feature a serious boost. It could already search the web and your Google Workspace files, but the new ‘Advanced Research’ mode is built for when you need to dig really deep.

Flip the switch for this advanced mode, and Claude tackles big questions differently. Instead of just one big search, it intelligently breaks your request down into smaller chunks, investigates each part thoroughly – using the web, your Google Docs, and now tapping into any apps you’ve connected via Integrations – before pulling it all together into a detailed report.

Now, this deeper digging takes a bit more time. While many reports might only take five to fifteen minutes, Anthropic says the really complex investigations could have Claude working away for up to 45 minutes. That might sound like a while, but compare it to the hours you might spend grinding through that research manually, and it starts to look pretty appealing.

Importantly, you can trust the results. When Claude uses information from any source – whether it’s a website, an internal doc, a Jira ticket, or a Confluence page – it gives you clear links straight back to the original. No more wondering where the AI got its information from; you can check it yourself.

These shiny new Integrations and the Advanced Research mode are rolling out now in beta for folks on Anthropic’s paid Max, Team, and Enterprise plans. If you’re on the Pro plan, don’t worry – access is coming your way soon.

Also worth noting: the standard web search feature inside Claude is now available everywhere, for everyone on any paid Claude.ai plan (Pro and up). No more geographical restrictions on that front.

Putting it all together, these updates and integrations show Anthropic is serious about making Claude genuinely useful in a professional context. By letting it plug directly into the tools we already use and giving it more powerful ways to analyse information, they’re pushing Claude towards being less of a novelty and more of an essential part of the modern toolkit.

(Image credit: Anthropic)

See also: Baidu ERNIE X1 and 4.5 Turbo boast high performance at low cost

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Training these AI agents presents significant hurdles, particularly when decisions span multiple steps and involve unpredictable feedback from the environment. While reinforcement learning (RL) has shown promise in static tasks like solving maths problems or generating code, its application to dynamic, multi-turn agent training has been less explored.   

Addressing this gap, a collaborative team from institutions including Northwestern University, Stanford University, Microsoft, and New York University has proposed StarPO (State-Thinking-Actions-Reward Policy Optimisation).

StarPO offers a generalised approach for training agents at the trajectory level (i.e. it optimises the entire sequence of interactions, not just individual actions.)

Accompanying this is RAGEN, a modular system built to implement StarPO. This enables the training and evaluation of LLM agents, particularly focusing on their reasoning capabilities under RL. RAGEN provides the necessary infrastructure for rollouts, reward assignment, and optimisation within multi-turn, stochastic (randomly determined) environments.

Minimalist environments, maximum insight

To isolate the core learning challenges from confounding factors like extensive pre-existing knowledge or task-specific engineering, the researchers tested LLMs using RAGEN in three deliberately minimalistic, controllable symbolic gaming environments:   

1. Bandit: A single-turn, stochastic task testing risk-sensitive symbolic reasoning. The agent chooses between options (like ‘Phoenix’ or ‘Dragon’ arms) with different, initially unknown, reward profiles.
2. Sokoban: A multi-turn, deterministic puzzle requiring foresight and planning, as actions (pushing boxes) are irreversible.
3. Frozen Lake: A multi-turn, stochastic grid navigation task where movement attempts can randomly fail, demanding planning under uncertainty.

These environments allow for clear analysis of how agents learn decision-making policies purely through interaction.   

Key findings: Stability, rollouts, and reasoning

The study yielded three significant findings concerning the training of self-evolving LLM agents:

The ‘Echo Trap’ and the need for stability

A recurring problem observed during multi-turn RL training was dubbed the “Echo Trap”. Agents would initially improve but then suffer performance collapse, overfitting to locally rewarded reasoning patterns. 

This was marked by collapsing reward variance, falling entropy (a measure of randomness/exploration), and sudden spikes in gradients (indicating training instability). Early signs included drops in reward standard deviation and output entropy.   

To combat this, the team developed StarPO-S, a stabilised version of the framework. StarPO-S incorporates:   

• Variance-based trajectory filtering: Focusing training on task instances where the agent’s behaviour shows higher uncertainty (higher reward variance), discarding low-variance, less informative rollouts. This improved stability and efficiency.   
• Critic incorporation: Using methods like PPO (Proximal Policy Optimisation), which employ a ‘critic’ to estimate value, generally showed better stability than critic-free methods like GRPO (Group Relative Policy Optimisation) in most tests.   
• Decoupled clipping and KL removal: Techniques adapted from other research (DAPO) involving asymmetric clipping (allowing more aggressive learning from positive rewards) and removing KL divergence penalties (encouraging exploration) further boosted stability and performance.   

StarPO-S consistently delayed collapse and improved final task performance compared to vanilla StarPO.   

Rollout quality is crucial

The characteristics of the ‘rollouts’ (simulated interaction trajectories used for training) significantly impact learning. Key factors identified include:   

• Task diversity: Training with a diverse set of initial states (prompts), but with multiple responses generated per prompt, aids generalisation. The sweet spot seemed to be moderate diversity enabling contrast between different outcomes in similar scenarios.   
• Interaction granularity: Allowing multiple actions per turn (around 5-6 proved optimal) enables better planning within a fixed turn limit, without introducing the noise associated with excessively long action sequences.   
• Rollout frequency: Using fresh, up-to-date rollouts that reflect the agent’s current policy is vital. More frequent sampling (approaching an ‘online’ setting) leads to faster convergence and better generalisation by reducing policy-data mismatch.

Maintaining freshness, alongside appropriate action budgets and task diversity, is key for stable training.   

Reasoning requires careful reward design

Simply prompting models to ‘think’ doesn’t guarantee meaningful reasoning emerges, especially in multi-turn tasks. The study found:

• Reasoning traces helped generalisation in the simpler, single-turn Bandit task, even when symbolic cues conflicted with rewards.   
• In multi-turn tasks like Sokoban, reasoning benefits were limited, and the length of ‘thinking’ segments consistently declined during training. Agents often regressed to direct action selection or produced “hallucinated reasoning” if rewards only tracked task success, revealing a “mismatch between thoughts and environment states.”

This suggests that standard trajectory-level rewards (often sparse and outcome-based) are insufficient. 

“Without fine-grained, reasoning-aware reward signals, agent reasoning hardly emerge[s] through multi-turn RL.”

The researchers propose that future work should explore rewards that explicitly evaluate the quality of intermediate reasoning steps, perhaps using format-based penalties or rewarding explanation quality, rather than just final outcomes.   

RAGEN and StarPO: A step towards self-evolving AI

The RAGEN system and StarPO framework represent a step towards training LLM agents that can reason and adapt through interaction in complex, unpredictable environments.

This research highlights the unique stability challenges posed by multi-turn RL and offers concrete strategies – like StarPO-S’s filtering and stabilisation techniques – to mitigate them. It also underscores the critical role of rollout generation strategies and the need for more sophisticated reward mechanisms to cultivate genuine reasoning, rather than superficial strategies or hallucinations.

While acknowledging limitations – including the need to test on larger models and optimise for domains without easily verifiable rewards – the work opens “a scalable and principled path for building AI systems” in areas demanding complex interaction and verifiable outcomes, such as theorem proving, software engineering, and scientific discovery.

(Image by Gerd Altmann)

See also: How does AI judge? Anthropic studies the values of Claude

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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While large language models popularised the concept of “agents” as tools that answer queries or retrieve information via methods such as Retrieval-Augmented Generation (RAG), Amazon envisions something more robust. The company defines agents not just as responders but as entities capable of performing tangible, multi-step tasks in diverse digital and physical environments.

“Our dream is for agents to perform wide-ranging, complex, multi-step tasks like organising a wedding or handling complex IT tasks to increase business productivity,” said Amazon.

Current market offerings often fall short, with many agents requiring continuous human supervision and their functionality dependent on comprehensive API integration—something not feasible for all tasks. Nova Act is Amazon’s answer to these limitations.

Alongside the model, Amazon is releasing a research preview of the Amazon Nova Act SDK. Using the SDK, developers can create agents capable of automating web tasks like submitting out-of-office notifications, scheduling calendar holds, or enabling automatic email replies.

The SDK aims to break down complex workflows into dependable “atomic commands” such as searching, checking out, or interacting with specific interface elements like dropdowns or popups. Detailed instructions can be added to refine these commands, allowing developers to, for instance, instruct an agent to bypass an insurance upsell during checkout.

To further enhance accuracy, the SDK supports browser manipulation via Playwright, API calls, Python integrations, and parallel threading to overcome web page load delays.

Nova Act: Exceptional performance on benchmarks

Unlike other generative models that showcase middling accuracy on complex tasks, Nova Act prioritises reliability. Amazon highlights its model’s impressive scores of over 90% on internal evaluations for specific capabilities that typically challenge competitors. 

Nova Act achieved a near-perfect 0.939 on the ScreenSpot Web Text benchmark, which measures natural language instructions for text-based interactions, such as adjusting font sizes. Competing models such as Claude 3.7 Sonnet (0.900) and OpenAI’s CUA (0.883) trail behind by significant margins.

Similarly, Nova Act scored 0.879 in the ScreenSpot Web Icon benchmark, which tests interactions with visual elements like rating stars or icons. While the GroundUI Web test, designed to assess an AI’s proficiency in navigating various user interface elements, showed Nova Act slightly trailing competitors, Amazon sees this as an area ripe for improvement as the model evolves.

Amazon stresses its focus on delivering practical reliability. Once an agent built using Nova Act functions as expected, developers can deploy it headlessly, integrate it as an API, or even schedule it to run tasks asynchronously. In one demonstrated use case, an agent automatically orders a salad for delivery every Tuesday evening without requiring ongoing user intervention.

Amazon sets out its vision for scalable and smart AI agents

One of Nova Act’s standout features is its ability to transfer its user interface understanding to new environments with minimal additional training. Amazon shared an instance where Nova Act performed admirably in browser-based games, even though its training had not included video game experiences. This adaptability positions Nova Act as a versatile agent for diverse applications.

This capability is already being leveraged in Amazon’s own ecosystem. Within Alexa+, Nova Act enables self-directed web navigation to complete tasks for users, even when API access is not comprehensive enough. This represents a step towards smarter AI assistants that can function independently, harnessing their skills in more dynamic ways.

Amazon is clear that Nova Act represents the first stage in a broader mission to craft intelligent, reliable AI agents capable of handling increasingly complex, multi-step tasks. 

Expanding beyond simple instructions, Amazon’s focus is on training agents through reinforcement learning across varied, real-world scenarios rather than overly simplistic demonstrations. This foundational model serves as a checkpoint in a long-term training curriculum for Nova models, indicating the company’s ambition to reshape the AI agent landscape.

“The most valuable use cases for agents have yet to be built,” Amazon noted. “The best developers and designers will discover them. This research preview of our Nova Act SDK enables us to iterate alongside these builders through rapid prototyping and iterative feedback.”

Nova Act is a step towards making AI agents truly useful for complex, digital tasks. From rethinking benchmarks to emphasising reliability, its design philosophy is centred around empowering developers to move beyond what’s possible with current-generation tools. 

See also: Anthropic provides insights into the ‘AI biology’ of Claude

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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This integration of web search functionality means Claude can now access the latest information to expand its knowledge base beyond its initial training data.

A key feature of this update is the emphasis on transparency and fact-checking. Anthropic highlights that “When Claude incorporates information from the web into its responses, it provides direct citations so you can easily fact check sources.”

Furthermore, Claude aims to streamline the information-gathering process for users. Instead of requiring users to manually sift through search engine results, “Claude processes and delivers relevant sources in a conversational format.”

Anthropic believes this enhancement will unlock a multitude of new use cases for Claude across various industries. They outlined several ways users can leverage Claude with web search:

• Sales teams: Can now “transform account planning and drive higher win rates through informed conversations with prospects by analysing industry trends to learn key initiatives and pain points.” This allows sales professionals to have more informed and persuasive conversations with potential clients.
• Financial analysts: Can “assess current market data, earnings reports, and industry trends to make better investment decisions and inform financial model assumptions.” Access to real-time financial data can improve the accuracy and timeliness of financial analysis.
• Researchers: Can “build stronger grant proposals and literature reviews by searching across primary sources on the web, spotting emerging trends and identifying gaps in the current literature.” This capability can accelerate the research process and lead to more comprehensive and insightful findings.
• Shoppers: Can “compare product features, prices, and reviews across multiple sources to make more informed purchase decisions.”

While the initial rollout is limited to paid users in the US, Anthropic assures that support for users on their free plan and more countries is coming soon.

To activate the web search feature, users simply need to “toggle on web search in your profile settings and start a conversation with Claude 3.7 Sonnet.” Once enabled, “When applicable, Claude will search the web to inform its response.”

This update aims to make Claude a more powerful and versatile tool for a wide range of tasks. By providing access to real-time information and ensuring transparency through citations, Anthropic is addressing key challenges and further solidifying Claude’s position as a leading AI assistant.

(Image credit: Anthropic)

See also: Hugging Face calls for open-source focus in the AI Action Plan

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The Yokohama platform release features teams of preconfigured AI agents designed to deliver immediate productivity gains. These agents operate on a single, unified platform, ensuring seamless integration and coordination across different business functions. The platform also includes capabilities to build, onboard, and manage the entire AI agent lifecycle, making it easier for enterprises to adopt and scale AI solutions.

Data is the lifeblood of AI, and ServiceNow recognises this by expanding its Knowledge Graph with advancements to its Common Service Data Model (CSDM). This expansion aims to break down barriers among data sources, enabling more connected and intelligent AI agents. By unifying data from various sources, ServiceNow’s platform ensures that AI agents can operate with a comprehensive view of the enterprise, driving more informed decisions and actions.

The growing need for ‘Guardian Agents’

According to Gartner, by 2028, 40% of CIOs will demand ‘Guardian Agents’ to autonomously track, oversee, or contain the results of AI agent actions. This underscores the growing need for a coordinated, enterprise-wide approach to AI deployment and management.

ServiceNow’s Yokohama release addresses this need by serving as the AI agent control tower for enterprises. The platform removes common roadblocks such as data fragmentation, governance gaps, and real-time performance challenges, ensuring seamless data connectivity with Workflow Data Fabric.

Unlike other AI providers that operate in silos or require complex integrations, ServiceNow AI Agents are built on a single, enterprise-wide platform. This ensures seamless data connectivity and provides a single view of all workflows, AI, and automation needs.

Amit Zavery, President, Chief Product Officer, and Chief Operating Officer at ServiceNow, commented: “Agentic AI is the new frontier. Enterprise leaders are no longer just experimenting with AI agents; they’re demanding AI solutions that can help them achieve productivity at scale.

“ServiceNow’s industry‑leading agentic AI framework meets this need by delivering predictability and efficiency from the start. With the combination of agentic AI, data fabric, and workflow automation all on one platform, we’re making it easier for organisations to embed connected AI where work happens and both measure and drive business outcomes faster, smarter, and at scale.”

New AI agents from ServiceNow aim to accelerate productivity

ServiceNow’s new AI Agents are now available to accelerate productivity at scale. These agents are designed to drive real outcomes for enterprise-wide use cases. For example:

• Security Operations (SecOps) expert AI agents: These agents transform security operations by streamlining the entire incident lifecycle, eliminating repetitive tasks, and empowering SecOps teams to focus on stopping real threats quickly.
• Autonomous change management AI agents: Acting like seasoned change managers, these agents generate custom implementation, test, and backout plans by analysing impact, historical data, and similar changes, ensuring seamless execution with minimal risk.
• Proactive network test & repair AI agents: These AI-powered troubleshooters automatically detect, diagnose, and resolve network issues before they impact performance.

ServiceNow AI Agent Orchestrator and AI Agent Studio are now generally available with expanded capabilities to govern the complete AI agent lifecycle.

These tools help to streamline the setup process with guided instructions, making it easier to design and configure new AI agents using natural language descriptions. Their expanded performance management capabilities include an analytics dashboard for visualising AI agent usage, quality, and value—ensuring that AI agent performance and ROI can be easily tracked.

At the core of the ServiceNow Platform is Workflow Data Fabric, enabling AI-powered workflows that integrate with an organisation’s data, regardless of the system or source. This fabric allows businesses to gain deeper insights through AI-driven contextualisation and decision intelligence while automating manual work and creating process efficiencies.

The Yokohama release continues to expand ServiceNow’s Knowledge Graph data capabilities with enhancements to its Common Service Data Model (CSDM). CSDM provides a standardised framework for managing IT and business services to accelerate quick, safe, and compliant technology deployments.

Several customers and partners have already seen the benefits of ServiceNow’s AI solutions. CANCOM, Cognizant, Davies, and Sentara have all praised the platform’s ability to drive efficiency, cost savings, and productivity. These organisations have successfully integrated ServiceNow’s AI agents into their operations.

Jason Wojahn, Global Head of the ServiceNow Business Group at Cognizant, said: “At Cognizant, we are helping companies harness the next phase of AI with agentic AI workflows that could bring unparalleled efficiency. We were the first to bring ServiceNow’s Workflow Data Fabric to market and are working to help our clients to seamlessly connect their data with AI.

“With the Yokohama release and the integration of AI agents onto the Now Platform, clients can now operate their agents virtually effortlessly with connected data, driving productivity and ROI across their entire business.”

Darrell Burnell, Group Head of Technology at Davies, added: “Agility is essential for Davies, given our work with clients in heavily regulated markets. We’ve transformed our agent experience with ServiceNow’s generative AI, deploying Now Assist for ITSM in just six weeks to streamline information retrieval and accelerate resolution times.”

ServiceNow’s Yokohama platform release is a step forward in the evolution of AI for business transformation. By unleashing new AI agents and expanding data capabilities, ServiceNow aims to empower businesses to achieve faster and smarter workflows to maximise impact.

(Image by Thomas Fengler)

See also: Opera introduces browser-integrated AI agent

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Rather than acting as a separate tool, Browser Operator is an extension of the browser itself—designed to empower users by automating repetitive tasks like purchasing products, completing online forms, and gathering web content.

Unlike server-based AI integrations which require sensitive data to be sent to third-party servers, Browser Operator processes tasks locally within the Opera browser.

Opera’s demonstration video showcases how Browser Operator can streamline an everyday task like buying socks. Instead of manually scrolling through product pages or filling out payment forms, users could delegate the entire process to Browser Operator—allowing them to shift focus to activities that matter more to them, such as spending time with loved ones.

Harnessing natural language processing powered by Opera’s AI Composer Engine, Browser Operator interprets written instructions from users and executes corresponding tasks within the browser. All operations occur locally on a user’s device, leveraging the browser’s own infrastructure to safely and swiftly complete commands.  

If Browser Operator encounters a sensitive step in the process, such as entering payment details or approving an order, it pauses and requests the user’s input. You also have the freedom to intervene and take control of the process at any time.  

Every step Browser Operator takes is transparent and fully reviewable, providing users a clear understanding of how tasks are being executed. If mistakes occur – like placing an incorrect order – you can further instruct the AI agent to make amends, such as cancelling the order or adjusting a form.

The key differentiators: Privacy, performance, and precision  

What sets Browser Operator apart from other AI-integrated tools is its localised, privacy-first architecture. Unlike competitors that depend on screenshots or video recordings to understand webpage content, Opera’s approach uses the Document Object Model (DOM) Tree and browser layout data—a textual representation of the webpage.  

This difference offers several key advantages:

• Faster task completion: Browser Operator doesn’t need to “see” and interpret pixels on the screen or emulate mouse movements. Instead, it accesses web page elements directly, avoiding unnecessary overhead and allowing it to process pages holistically without scrolling.

• Enhanced privacy: With all operations conducted on the browser itself, user data – including logins, cookies, and browsing history – remains secure on the local device. No screenshots, keystrokes, or personal information are sent to Opera’s servers.

• Easier interaction with page elements: The AI can engage with elements hidden from the user’s view, such as behind cookie popups or verification dialogs, enabling seamless access to web page content.

By enabling the browser to autonomously perform tasks, Opera is taking a significant step forward in making browsers “agentic”—not just tools for accessing the internet, but assistants that actively enhance productivity.  

See also: You.com ARI: Professional-grade AI research agent for businesses

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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According to the platform’s published strategy, the company will focus on three key principles – AI Blocks, Product Power-ups, and a Digital Workforce. Its aims are to “accelerate its vision to democratise the power of software.” By making its software more accessible and usable for its users, monday.com hopes to address the challenges encountered by businesses and work forces in teams of all sizes, streamline workflows and increase productivity.

The latest AI strategies are designed to help small and medium-sized businesses (SMBs) and mid-market companies grow and adapt quickly without the need to hire more staff. According to monday.com, AI will boost process times that are typically slowed by the scale of larger enterprises and Fortune 500 companies.

In line with monday.com’s stated goal to “democratise access to AI,” the platform’s approach is to be user-friendly, allowing any users regardless of technical knowledge to create, customise, and use AI tools in their workflows.

Daniel Lereya, Chief Product and Technology Officer at monday.com spoke about the company’s approach to making software accessible to everyone. “By embedding intelligence into the products our customers already know, use, and love, AI will accelerate our mission to democratise the power of software,” he said.

“With a majority of our customers in non-tech industries, they’re looking to us to lead them through the AI transformation. Our productisation of AI ensures that intelligence serves our customers and not the other way around.”

Of the three principles ‘AI Blocks’ are customisable AI tools that can be added to existing workflows, regardless of technical knowledge. The AI Blocks feature capabilities like “Categorise” and “Extract,’ letting users analyse data, and recognise patterns in a few clicks.

The second principle, ‘Product Power-ups,’ refers to the integration of AI features directly into monday.com’s existing suite of products. This is designed to help the company’s customers address challenges in areas like CRM data automation, resource management, predictive risk management, and real-time service ticket resolution. The features could help teams make quicker, more informed decisions, streamline workflows and increase efficiency.

Finally, ‘Digital Workforce’ is a collection of AI agents that operate to support users and customers. Digital Workforce is can handle specific tasks that are automatable, including project risk analysis, the identification of ongoing customer service issues, and helping move delayed sales deals forward.

monday.com is planning the launch of ‘monday Expert’ in March, its first AI agent that’s designed to support the onboarding of new users, and perform some tasks on behalf of users.

The company claims its AI capabilities have had a notable effect, with reports of monday.com users performing around 10 million AI-driven actions in 2024. The numbers of AI use instances nearly tripled each quarter of 2024, indicating a rapid rise in the use of AI in the CRM platform.

(Image: “Monday – Back to work!” by tinto is licensed under CC BY 2.0.)

ion Summit: Leaders call for unity and equitable development

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Designed around a new model called the Computer-Using Agent (CUA), Operator combines GPT-4o’s vision recognition with advanced reasoning capabilities—allowing it to function as a virtual “human-in-the-browser.” Yet, for all its innovation, industry experts see room for refinement.

Yiannis Antoniou, Head of AI, Data, and Analytics at specialist consultancy Lab49, shared his insights on Operator’s significance and positioning in the competitive landscape of agent AI systems.

Agentic AI through a familiar interface

“OpenAI’s announcement of Operator, its latest foray into the agentic AI wars, is both fascinating and incomplete,” said Antoniou, who has over two decades of experience designing AI systems for financial services firms.

“Clearly influenced by Anthropic Claude’s Computer Use system, introduced back in October, Operator streamlines the experience by removing the need for complex infrastructure and focusing on a familiar interface: the browser.”

By designing Operator to operate within an environment users already understand, the web browser, OpenAI sidesteps the need for bespoke APIs or integrations.

“By leveraging the world’s most popular interface, OpenAI enhances the user experience and captures immediate interest from the general public. This browser-centric approach creates significant potential for widespread adoption, something Anthropic – despite its early-mover advantage – has struggled to achieve.”

Unlike some competing systems that may feel technical or niche in their application, Operator’s browser-focused framework lowers the barrier to entry and is a step forward in OpenAI’s efforts to democratise AI.

Unique take on usability and security

One of the hallmarks of Operator is its emphasis on adaptability and security, implemented through human-in-the-loop protocols. Antoniou acknowledged these thoughtful usability features but noted that more work is needed.

“Architecturally, Operator’s browser integration closely mirrors Claude’s system. Both involve taking screenshots of the user’s browser and sending them for analysis, as well as controlling the screen via virtual keystrokes and mouse movements. However, Operator introduces thoughtful usability touches. 

“Features like custom instructions for specific websites add a layer of personalisation, and the emphasis on human-in-the-loop safeguards against unauthorised actions – such as purchases, sending emails, or applying for jobs – demonstrate OpenAI’s awareness of potential security risks posed by malicious websites, but more work is clearly needed to make this system widely safe across a variety of scenarios.”

OpenAI has implemented a multi-layered safety framework for Operator, including takeover mode for secure inputs, user confirmations prior to significant actions, and monitoring systems to detect adversarial behavior. Furthermore, users can delete browsing data and manage privacy settings directly within the tool.

However, Antoniou emphasised that these measures are still evolving—particularly as Operator encounters complex or sensitive tasks. 

OpenAI Operator further democratises AI

Antoniou also sees the release of Operator as a pivotal moment for the consumer AI landscape, albeit one that is still in its early stages. 

“Overall, this is an excellent first attempt at building an agentic system for everyday users, designed around how they naturally interact with technology. As the system develops – with added capabilities and more robust security controls – this limited rollout, priced at $200/month, will serve as a testing ground. 

“Once matured and extended to lower subscription tiers and the free version, Operator has the potential to usher in the era of consumer-facing agents, further democratising AI and embedding it into daily life.”

Designed initially for Pro users at a premium price point, Operator provides OpenAI with an opportunity to learn from early adopters and refine its capabilities.

Antoniou noted that while $200/month might not yet justify the system’s value for most users, investment in making Operator more powerful and accessible could lead to significant competitive advantages for OpenAI in the long run.

“Is it worth $200/month? Perhaps not yet. But as the system evolves, OpenAI’s moat will grow, making it harder for competitors to catch up. Now, the challenge shifts back to Anthropic and Google – both of whom have demonstrated similar capabilities in niche or engineering-focused products – to respond and stay in the game,” he concludes.

As OpenAI continues to fine-tune Operator, the potential to revolutionise how people interact with technology becomes apparent. From collaborations with companies like Instacart, DoorDash, and Uber to use cases in the public sector, Operator aims to balance innovation with trust and safety.

While early limitations and pricing may deter widespread adoption for now, these hurdles might only be temporary as OpenAI commits to enhancing usability and accessibility over time.

See also: OpenAI argues against ChatGPT data deletion in Indian court

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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A year after introducing the Gemini 1.0 model, this major upgrade incorporates enhanced multimodal capabilities, agentic functionality, and innovative user tools designed to push boundaries in AI-driven technology.

Leap towards transformational AI  

Reflecting on Google’s 26-year mission to organise and make the world’s information accessible, Pichai remarked, “If Gemini 1.0 was about organising and understanding information, Gemini 2.0 is about making it much more useful.”

Gemini 1.0, released in December 2022, was notable for being Google’s first natively multimodal AI model. The first iteration excelled at understanding and processing text, video, images, audio, and code. Its enhanced 1.5 version became widely embraced by developers for its long-context understanding, enabling applications such as the productivity-focused NotebookLM.

Now, with Gemini 2.0, Google aims to accelerate the role of AI as a universal assistant capable of native image and audio generation, better reasoning and planning, and real-world decision-making capabilities. In Pichai’s words, the development represents the dawn of an “agentic era.”

“We have been investing in developing more agentic models, meaning they can understand more about the world around you, think multiple steps ahead, and take action on your behalf, with your supervision,” Pichai explained.

Gemini 2.0: Core features and availability

At the heart of today’s announcement is the experimental release of Gemini 2.0 Flash, the flagship model of Gemini’s second generation. It builds upon the foundations laid by its predecessors while delivering faster response times and advanced performance.

Gemini 2.0 Flash supports multimodal inputs and outputs, including the ability to generate native images in conjunction with text and produce steerable text-to-speech multilingual audio. Additionally, users can benefit from native tool integration such as Google Search and even third-party user-defined functions.

Developers and businesses will gain access to Gemini 2.0 Flash via the Gemini API in Google AI Studio and Vertex AI, while larger model sizes are scheduled for broader release in January 2024.

For global accessibility, the Gemini app now features a chat-optimised version of the 2.0 Flash experimental model. Early adopters can experience this updated assistant on desktop and mobile, with a mobile app rollout imminent.

Products such as Google Search are also being enhanced with Gemini 2.0, unlocking the ability to handle complex queries like advanced math problems, coding enquiries, and multimodal questions.

Comprehensive suite of AI innovations  

The launch of Gemini 2.0 comes with compelling new tools that showcase its capabilities.

One such feature, Deep Research, functions as an AI research assistant, simplifying the process of investigating complex topics by compiling information into comprehensive reports. Another upgrade enhances Search with Gemini-enabled AI Overviews that tackle intricate, multi-step user queries.

The model was trained using Google’s sixth-generation Tensor Processing Units (TPUs), known as Trillium, which Pichai notes “powered 100% of Gemini 2.0 training and inference.”

Trillium is now available for external developers, allowing them to benefit from the same infrastructure that supports Google’s own advancements.

Pioneering agentic experiences  

Accompanying Gemini 2.0 are experimental “agentic” prototypes built to explore the future of human-AI collaboration, including:

• Project Astra: A universal AI assistant

First introduced at I/O earlier this year, Project Astra taps into Gemini 2.0’s multimodal understanding to improve real-world AI interactions. Trusted testers have trialled the assistant on Android, offering feedback that has helped refine its multilingual dialogue, memory retention, and integration with Google tools like Search, Lens, and Maps. Astra has also demonstrated near-human conversational latency, with further research underway for its application in wearable technology, such as prototype AI glasses.

• Project Mariner: Redefining web automation 

Project Mariner is an experimental web-browsing assistant that uses Gemini 2.0’s ability to reason across text, images, and interactive elements like forms within a browser. In initial tests, it achieved an 83.5% success rate on the WebVoyager benchmark for completing end-to-end web tasks. Early testers using a Chrome extension are helping to refine Mariner’s capabilities while Google evaluates safety measures that ensure the technology remains user-friendly and secure.

• Jules: A coding agent for developers  

Jules, an AI-powered assistant built for developers, integrates directly into GitHub workflows to address coding challenges. It can autonomously propose solutions, generate plans, and execute code-based tasks—all under human supervision. This experimental endeavour is part of Google’s long-term goal to create versatile AI agents across various domains.

• Gaming applications and beyond  

Extending Gemini 2.0’s reach into virtual environments, Google DeepMind is working with gaming partners like Supercell on intelligent game agents. These experimental AI companions can interpret game actions in real-time, suggest strategies, and even access broader knowledge via Search. Research is also being conducted into how Gemini 2.0’s spatial reasoning could support robotics, opening doors for physical-world applications in the future.

Addressing responsibility in AI development

As AI capabilities expand, Google emphasises the importance of prioritising safety and ethical considerations.

Google claims Gemini 2.0 underwent extensive risk assessments, bolstered by the Responsibility and Safety Committee’s oversight to mitigate potential risks. Additionally, its embedded reasoning abilities allow for advanced “red-teaming,” enabling developers to evaluate security scenarios and optimise safety measures at scale.

Google is also exploring safeguards to address user privacy, prevent misuse, and ensure AI agents remain reliable. For instance, Project Mariner is designed to prioritise user instructions while resisting malicious prompt injections, preventing threats like phishing or fraudulent transactions. Meanwhile, privacy controls in Project Astra make it easy for users to manage session data and deletion preferences.

Pichai reaffirmed the company’s commitment to responsible development, stating, “We firmly believe that the only way to build AI is to be responsible from the start.”

With the Gemini 2.0 Flash release, Google is edging closer to its vision of building a universal assistant capable of transforming interactions across domains.

See also: Machine unlearning: Researchers make AI models ‘forget’ data

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The report places the UK ahead of its G7 counterparts in terms of AI adoption but also underscores areas ripe for improvement, such as support for SMEs, fostering cross-sector partnerships, and investing in talent development.

Zahra Bahrololoumi CBE, UKI CEO at Salesforce, commented: “Agentic AI is revolutionising enterprise software by enabling humans and agents to collaborate seamlessly and drive customer success.

“The UK AI Readiness Index positively highlights that the UK has both the vision and infrastructure to be a powerhouse globally in AI, and lead the current third wave of agentic AI.”

UK AI adoption sets the stage for agentic revolution

The Index details how both the public and private sectors in the UK have embraced AI’s transformative potential. With a readiness score of 65.5, surpassing the G7 average of 61.2, the UK is establishing itself as a hub for large-scale AI projects, driven by a robust innovation culture and pragmatic regulatory approaches.

The government has played its part in maintaining a stable and secure environment for tech investment. Initiatives such as the AI Safety Summit at Bletchley Park and risk-oriented AI legislation showcase Britain’s leadership on critical AI issues like transparency and privacy.

Business readiness is equally impressive, with UK industries scoring 52, well above the G7 average of 47.8. SMEs in the UK are increasingly prioritising AI adoption, further bolstering the nation’s stance in the international AI arena.

Adam Evans, EVP & GM of Salesforce AI Platform, is optimistic about the evolution of agentic AI. Evans foresees that, by 2025, these agents will become business-aware—expertly navigating industry-specific challenges to execute meaningful tasks and decisions.

Investments fuelling AI growth

Salesforce is committing $4 billion to the UK’s AI ecosystem over the next five years. Since establishing its UK AI Centre in London, Salesforce says it has engaged over 3,000 stakeholders in AI training and workshops.

Key investment focuses include creating a regulatory bridge between the EU’s rules-based approach and the more relaxed US approach, and ensuring SMEs have the resources to integrate AI. A strong emphasis also lies on enhancing digital skills and centralising training to support the AI workforce of the future.

Feryal Clark, Minister for AI and Digital Government, said: “These findings are further proof the UK is in prime position to take advantage of AI, and highlight our strength in spurring innovation, investment, and collaboration across the public and private sector.

“There is a global race for AI and we’ll be setting out plans for how the UK can use the technology to ramp-up adoption across the economy, kickstart growth, and build an AI sector which can scale and compete on the global stage.”

Antony Walker, Deputy CEO at techUK, added: “To build this progress, government and industry must collaborate to foster innovation, support SMEs, invest in skills, and ensure flexible regulation, cementing the UK’s leadership in the global AI economy.”

Agentic AI boosting UK business productivity 

Capita, Secret Escapes, Heathrow, and Bionic are among the organisations that have adopted Salesforce’s Agentforce to boost their productivity.

Adolfo Hernandez, CEO of Capita, said: “We want to transform Capita’s recruitment process into a fast, seamless and autonomous experience that benefits candidates, our people, and our clients.

“With autonomous agents providing 24/7 support, our goal is to enable candidates to complete the entire recruitment journey within days as opposed to what has historically taken weeks.

Secret Escapes, a curator of luxury travel deals, finds autonomous agents crucial for personalising services to its 60 million European members.

Kate Donaghy, Head of Business Technology at Secret Escapes, added: “Agentforce uses our unified data to automate routine tasks like processing cancellations, updating booking information, or even answering common travel questions about luggage, flight information, and much more—freeing up our customer service agents to handle more complex and last-minute travel needs to better serve our members.”

The UK’s AI readiness is testament to the synergy between government, business, and academia. To maintain its leadership, the UK must sustain its focus on collaboration, skills development, and innovation. 

(Photo by Matthew Wiebe)

See also: Generative AI use soars among Brits, but is it sustainable?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The research – conducted by MHP – surveyed 4,700 car drivers across China, the US, Germany, the UK, Italy, Sweden, and Poland, revealing significant geographical disparities in AI acceptance and understanding.

According to the study, while AI is becoming integral to modern vehicles, European consumers remain hesitant about its implementation and value proposition.

Regional disparities

The study found that 48 percent of Chinese respondents view in-car AI predominantly as an opportunity, while merely 23 percent of European respondents share this optimistic outlook. In Europe, 39 percent believe AI’s opportunities and risks are broadly balanced, while 24 percent take a negative stance, suggesting the risks outweigh potential benefits.

Understanding of AI technology also varies significantly by region. While over 80 percent of Chinese respondents claim to understand AI’s use in cars, this figure drops to just 54 percent among European drivers, highlighting a notable knowledge gap.

Marcus Willand, Partner at MHP and one of the study’s authors, notes: “The figures show that the prospect of greater safety and comfort due to AI can motivate purchasing decisions. However, the European respondents in particular are often hesitant and price-sensitive.”

The willingness to pay for AI features shows an equally stark divide. Just 23 percent of European drivers expressed willingness to pay for AI functions, compared to 39 percent of Chinese drivers. The study suggests that most users now expect AI features to be standard rather than optional extras.

Dr Nils Schaupensteiner, Associated Partner at MHP and study co-author, said: “Automotive companies need to create innovations with clear added value and develop both direct and indirect monetisation of their AI offerings, for example through data-based business models and improved services.”

In-vehicle AI opportunities

Despite these challenges, traditional automotive manufacturers maintain a trust advantage over tech giants. The study reveals that 64 percent of customers trust established car manufacturers with AI implementation, compared to 50 percent for technology firms like Apple, Google, and Microsoft.

The research identified several key areas where AI could provide significant value across the automotive industry’s value chain, including pattern recognition for quality management, enhanced data management capabilities, AI-driven decision-making systems, and improved customer service through AI-powered communication tools.

“It is worth OEMs and suppliers considering the opportunities offered by the new technology along their entire value chain,” explains Augustin Friedel, Senior Manager and study co-author. “However, the possible uses are diverse and implementation is quite complex.”

The study reveals that while up to 79 percent of respondents express interest in AI-powered features such as driver assistance systems, intelligent route planning, and predictive maintenance, manufacturers face significant challenges in monetising these capabilities, particularly in the European market.

See also: MIT breakthrough could transform robot training

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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