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Yahoo
26 minutes ago
- Yahoo
Zoho Launches Zia LLM and Deepens AI Portfolio with Prebuilt Agents, Custom Agent Builder, MCP, and Marketplace
Homegrown, Business-Optimized Technology Addresses Needs for Developers and End Users While Enhancing Platform Capabilities AUSTIN, Texas, July 17, 2025--(BUSINESS WIRE)--Zoho Corporation, a global technology company, today announced additional investments and offerings in AI, including Zia LLM, a proprietary large language model; Zia Agents, with 25+ ready-to-deploy AI-powered agents available in Agent Marketplace; Zia Agent Studio, a no-code agent builder; and a model context protocol (MCP) server to open up Zoho's vast library of actions to third-party agents. These capabilities and investments are designed to help organizations fully realize and maximize the value of contextual, assistive and agentic AI technology. Immediately impacting daily workflows for diverse roles and use cases, Zoho's latest AI developments deliver operational and financial efficiencies across entire organizations. "Today's announcement emphasizes Zoho's longstanding aim to build foundational technology focused on protection of customer data, breadth and depth of capabilities, and value," said Mani Vembu, CEO at Zoho. "Because Zoho's AI initiatives are developed internally, we are able to provide customers with cutting-edge tool sets without compromising data privacy and organizational flexibility, democratizing the latest technology on a global scale." Zia LLM, Built from the Ground Up and Optimized for Business Zoho has successfully launched its own large language model, Zia LLM, built completely in-house by leveraging NVIDIA's AI accelerated computing platform. Trained with Zoho product use cases in mind—ranging from structured data extraction, summarization, RAG, and code generation—Zia LLM is comprised of three models with 1.3 billion, 2.6 billion and 7 billion parameters, each separately trained and optimized for contextual applicability that benchmark competitively against comparable open source models in the market. The three models allow Zoho to always optimize the right model for the right user context, striking the proper balance between power and resource management. This focus on right-sizing the model is an ongoing development strategy for Zoho. In addition to Zia LLM, Zoho is announcing two proprietary Automatic Speech Recognition (ASR) models for speech-to-text conversion for both English and Hindi. Optimized to perform on a low computer load without compromising on accuracy, the models benchmark up to 75% better than comparable models across standard tests. Language support for additional languages will be coming in the future. While Zoho supports many LLM integrations for users, including ChatGPT, Llama, and DeepSeek, Zia LLM continues Zoho's commitment to data privacy by allowing customers to keep their data on Zoho servers, leveraging the latest AI capabilities without sending their data to AI cloud providers. Zia LLM will be deployed across Zoho's data centers in the US, India, and Europe. The model is currently testing for internal use cases across Zoho's broad app portfolio, and will be available for customer use in coming months. Effective Native AI Agents Ready for Use To enable immediate adoption of agentic technology, Zoho has developed a roster of AI agents contextually baked right into its products. These agents can be used across various business activities, handling relevant actions based on real-life organizational roles (including sales development, customer support, and account management). Agents available today include: New Version of Ask Zia: The latest version of Zoho's platform-wide conversational AI assistant, Ask Zia's new BI skills are tailored to data engineers, analysts, and data scientists, yet supports any user within an organization. Ask Zia is now equipped with capabilities that directly address the unique pain points faced by each persona, whether it's building end-to-end data pipelines for engineers, analyzing data, creating reports and dashboards in an interactive conversation mode for analysts, or helping to jump start building ML models for data scientists. Customer Service Agent: With the ability to process incoming customer requests, understand the context behind them, and either answer them directly or triage them to a human rep, the Customer Service Agent for Zoho Desk provides an efficient yet reliable first line of assistance, paving the way for quicker responses and resolutions. AI Agent Studio and Marketplace First announced earlier in 2025, Zoho has further simplified the Zia Agent Studio experience to be fully prompt-based (with the option to use low-code) and include ready-made access to over 700 actions across Zoho's products. Agents built by users can be deployed autonomously, triggered through button click or rule-based automation, or summoned within customer conversations. At the time of deployment, an agent can also be provisioned as a digital employee. Digital Employees respect defined user access permissions, maintaining the same permissions structures already defined within the organization. Admins are able to perform behavioral audits as well as performance and impact analyses on Digital Employees, ensuring that every agent is working as effectively as possible and within clear guardrails. Zoho Marketplace, which supplies over 2500 reliable extensions and integrations for Zoho users, now houses the Agent Marketplace, a dedicated section for AI agents that can be deployed by customers quickly. Ecosystem partners, ISVs, and individual developers will soon be able to create agents and host them on the Zia Agents Marketplace, further simplifying the adoption of agentic technology by organizations. Some pre-built agents created with Zia Agent Studio (and available on the Zia Agent Marketplace) are: Revenue Growth Specialist: Uncovers opportunities for upsell and cross-sell across existing customers, recommending the best marketing approach for each customer. Deal Analyzer: Analyze deals and provide insights such as win probability, next best action, and follow-up suggestions. Candidate Screener: Intelligently identifies and ranks the most suitable candidates for a specific job opening based on role requirements, skills, experience, and other key attributes. Zoho will continue to add more pre-built agents to the Agent Marketplace over time to cover several valuable core and utility use cases across various business functions. The full list of available agents can be found under Additional Documentation. With over 55 applications across one ecosystem, users can build agents to meet their organization's every need, no matter how specific. With Zia Agent Studio, Zoho users have access to the same tools as Zoho's developers, ensuring that any agent a customer dreams of can be created with ease. Interoperability with MCP Zoho has adopted the model context protocol (MCP), offering its own MCP server with a rich action library across several applications, allowing any MCP client to tap into data and actions from various Zoho apps while respecting the customer's defined permission structures. Zoho's MCP server has a library of actions from more than 15 Zoho applications exposed during Early Access. With Zoho Flow, third party tools are also exposed. Additional Zoho applications will be onboarded in the coming months. Furthermore, Zoho Analytics now offers support for a local MCP server. Roadmap In the short term, Zoho will regularly scale Zia LLM's model sizes, starting with the first of several planned parameter increases by the end of 2025. Future planned releases include expanding the available languages used by the speech-to-text model, beginning with languages spoken primarily across Europe and India, as well as the introduction of a reasoning language model (RLM). Additional skills will be added to Ask Zia, allowing it to act as an assistant to Finance teams and Customer Support teams, with more skills added in the future. Support for the Agent2Agent (A2A) protocol will be implemented, allowing for Zia Agents to interact and collaborate with each other, as well as collaborate with agents on other platforms. Additional Documentation Zia Agent Marketplace - Full list of available agents at launch Disclaimer All trademarks, product names, and company names cited herein are the property of their respective owners. Availability and Pricing Zia LLM will be available to Zoho customers in the coming months. Zia Agents, Zia Agent Studio, Agent Marketplace, and Zoho MCP Server are being rolled out to customers who are currently on the early access waiting list. General availability for these offerings is expected towards the end of 2025. Zoho expects to study the usage patterns of these customers across use cases, industries, geographical regions, and sizes during this early access phase. A pricing structure for these offerings can be expected at the time of general availability. Zoho Artificial Intelligence Differentiation Zoho is committed to designing and incorporating artificial intelligence guided by the principles of customer privacy and value. Our generic AI models across contextual, assistive, and agentic AI, are not trained on consumer data and do not retain customer information. Zoho builds AI tools with usefulness in mind, striking a balance between providing AI technology that assists workers while right-sizing models that don't require burdening consumers with additional costs. Zoho Privacy Pledge Zoho respects user privacy and does not have an ad-revenue model in any part of its business, including its free products. The company owns and operates its data centers, ensuring complete oversight of customer data, privacy, and security. More than 130 million users around the world, across hundreds of thousands of companies, rely on Zoho everyday to run their businesses, including Zoho itself. For more information, please visit: About Zoho With 55+ apps in nearly every major business category, Zoho Corporation is one of the world's most prolific technology companies. Headquartered in Austin, Texas, with international headquarters in Chennai, India, Zoho is privately held and profitable with more than 18,000 employees. For more information, please visit: View source version on Contacts Media Contacts: Sandra Loslo@ Nanya Srivastavananya.s@ Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Forbes
29 minutes ago
- Forbes
Task-Sharing Of Therapy Gets Boosted Via New Guidebook By Google And McKinsey On AI For Mental Health
Moving toward task-sharing arrangements when it comes to expanding the availability of mental health ... More therapy services throughout the globe. In today's column, I examine a rising interest in parsing out the activities of performing mental health therapy, of which AI could be a handy tool in assisting the enactment of labor-based task-sharing arrangements. Note that the AI usage in this approach isn't actively enlisted to perform therapy and instead is simply used for subtle guidance when enlisting new labor to aid therapy. The AI is relegated principally to administrative tasks. Here's the deal. The available supply of mental health professionals is woefully insufficient to meet the growing needs for mental health therapy services. One possible solution is to bring non-specialists into the fold and allocate some of the therapeutic tasks to them, doing so cautiously and sparingly. This involves a potentially significant logistical and management-focused effort, and thus, the use of AI could be advantageous to streamline the arduous task-sharing endeavor (well, only if the AI is used intelligently). Let's talk about it. This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). AI And Mental Health Therapy As a quick background, I've been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that produces mental health advice and performs AI-driven therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For a quick summary of some of my posted columns on this evolving topic, see the link here, which briefly recaps about forty of the over one hundred column postings that I've made on the subject. There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors too. I frequently speak up about these pressing matters, including in an appearance last year on an episode of CBS's 60 Minutes, see the link here. If you are new to the topic of AI for mental health, you might want to consider reading my recent analysis of the field, which also recounts a highly innovative initiative at the Stanford University Department of Psychiatry and Behavioral Sciences called AI4MH; see the link here. Sharing Tasks With Added Labor There is no doubt that we don't have enough mental health professionals at this time. The formal pipeline of bringing in, training, and making available newly produced therapists is generally slow and not conducive to meeting the rapidly rising needs for therapeutic services. People often have a hard time finding a qualified therapist, they have difficulty booking time with the therapist, and they otherwise discover that mental health professionals are sparse in comparison to the abundant demand. What can be done? Trying to push more trainees through the pipeline is one option. Turns out this is still going to be a bottleneck. The road is bumpy, and some will likely inevitably drop out of the process. In any case, all manner of avenues are being pursued to rachet up the production process. Meanwhile, another idea is taking shape. Suppose that some of the tasks performed by therapists could be allocated to non-specialists. We could provide some limited level of training and get this additional labor pool going in record time. They would be the additional arms and legs of actual therapists. Each therapist is, in a sense, magnified manyfold by leaning into added labor to assist in certain kinds of therapy tasks and subtasks. The moniker given to this method or approach is known as task-sharing. Mental health professionals can opt to task-share with non-specialists. This must be done mindfully. A therapist ought not hand out the essence of conducting therapy. On the other hand, tasks such as scheduling clients, writing notes, and undertaking various administrative chores could sensibly be relegated to the added labor. Sounds like a great way to cope with the pent-up demand for therapy. Slippery Slope And Watering Down Not everyone necessarily agrees that task-sharing in the mental health domain is the wisest of choices. One concern is that the effort by mental health professionals to manage other non-specialist labor is going to undercut the time they might have spent performing therapy. Perhaps some therapists will become more akin to labor managers rather than doing actual therapy. They will get bogged down in selecting the labor, training the labor, guiding the labor, correcting the labor, and so on. Less time for client therapy. Another qualm is that there is a likely slippery slope involved. It happens this way. A therapist finds a non-specialist who does good work on administrative chores. After a while, the therapist gives the non-specialist increasing duties. Trust is there. Step by step, the therapist inches the non-specialist into the practice of therapy per se. The therapist didn't do this straight away; it was a slippery slope. The therapy being performed by the therapist in combination with their non-specialist gets watered down. Clients and patients don't realize what is occurring. They are reliant on the therapist and assume that the therapist is doing what is right. Meeting with the non-specialist is done under the banner of the actual therapist. These and other downsides and gotchas are aspects that need to be cautiously considered when going on the path of task-sharing in the mental health realm. Proceeding With Task-Sharing Assume that mental health professionals desirous of doing task-sharing are fully aware of the various limitations and potential shortcomings. I say that for the sake of this discussion. Reality is different, and please realize that not all mental health professionals pursuing the innovative approach will do so with their eyes wide open. I wish they would (I'll say more about this at the conclusion, herein). Given the assumption that the overall tactics and strategies are understood, what can be done to aid the task-sharing pursuit? One answer is that we could include AI in the mix. For the mainstay activities involved in task-sharing of mental health services, I will walk you through how it is that AI can be beneficial. The AI doesn't have to be used in every nook and cranny. That being said, we dare not overlook tasks and subtasks that could be constructively boosted due to sensibly incorporating AI. Observe that I mentioned that the AI needs to be sensibly incorporated. If you merely toss AI in this realm in a scattergun fashion, do not expect good results. AI could end up being a distractor. The AI could even be negative, causing troubles and introducing errors that otherwise might not have arisen. AI is never a silver bullet that solves all problems. The use of AI must be done judiciously. Watch for issues. Plan properly. Keep on top of what the AI is doing. And so on. Handy Field Guide On AI In Task-Sharing Fortunately, a newly released field guide provides handy insights for incorporating AI into the task-sharing of mental health therapy. The guide is entitled 'Mental Health And AI Field Guide' and was devised by Grand Challenges Canada, McKinsey Health Institute, and Google, posted online July 7, 2025, and included these selected key points (excerpts): You can perhaps see from those excerpted points that the new guide is full of useful insights. It provides important indications and offers real-world examples. The aim is to get the topic of task-sharing on the table and illuminate the role of AI in that exciting and emerging endeavor. For those of you who are researchers in psychology, psychiatry, cognitive sciences, artificial intelligence, etc., you might contemplate performing research that would empirically examine the use of AI in this task-sharing model. We need to have rigorous studies that shine the light on what works and what doesn't. There is ample opportunity to conduct fresh and original research in AI for mental health by tackling aspects of this particular topic. I look forward to seeing your incisive research results. The Task-Sharing Model According to the field guide that I noted above, the authors have opted to present a task-sharing model that consists of six major phases: You can think of this model as a typical life-cycle systems approach. The life cycle starts when you first conceive of doing task-sharing. In the first phase, you would take an outlined standardized set of tasks and adapt those to the situation at hand. Each situation will differ. If you are in a low-resource circumstance, that will dictate what options you have available. In a high-resource setting, you undoubtedly have more choices of what to do. After completing the first phase, you move to the second phase and identify the non-specialist candidates for serving in the task-sharing arrangement. They become your trainees. The third phase entails training them in whatever tasks have been parceled out. The fourth phase has you assigning the trained non-specialists to their respective tasks. The fifth phase involves monitoring their performance and undertaking interventions as required. The last phase is the completion of the program. This involves tying up any final aspects. You would hopefully do a lessons-learned and be prepared to start up another similar program at a later date. AI Infused Into The Model Let's put on our AI thinking caps. How could AI be useful to the six phases? Easy-peasy. According to the guidebook, here are some crucial considerations (the headings are mine, the AI-related task is their suggestion): There are a lot more places where AI can be utilized in the six phases of the model. I wanted to mainly whet your appetite. Look at the guide if you'd like to see more details. AI As Therapist The 800-pound gorilla in the mental health arena consists of asking the unabashed question of what degree AI should play a role in conducting therapy. I've emphasized that we are entering into an era that disrupts the classic duo of therapist-patient and is moving us into the new era of the triad, consisting of the therapist-AI-patient relationship (see the link here). AI is going to increasingly be in the middle of therapy. Like it or not. I bring this up because the initial model of task-sharing seems to edge around the immersion of AI into the roots of therapy itself. Probably the closest it gets is when the AI provides on-the-spot recommendations for care providers. That's dipping a toe into the therapy milieu. Upgrading Task-Sharing To AI-Driven Think about the task-sharing arrangement in the framework of AI as a therapist, including these thought-provoking points: Lots of tough questions are facing us, sooner rather than later. AI As Mover And Shaker Task-sharing is a thoughtful means of coping with the imbalance between the need for mental health therapy and the prevailing constrained pool of available mental health professionals. If done properly, it is possible to greatly magnify a set of therapists into a vast array of extended therapist-like addons. The catch is that it is all still labor-based. How much added labor can be mustered? How well will that added labor perform their assigned tasks? How much time shall be usurped from therapists to keep the added labor on target? Etc. AI, in contrast, is essentially infinitely scalable. All you need to do is add more computational power, and you can immensely scale until the cows come home. Of course, you must ensure that the thing you are scaling is going to be doing the right thing. Scaling something sour and dour will insidiously spread sourness and dourness to a wider audience. What Are Therapists To Be Or Not To Be A final thought for now. William Shakespeare famously said this: 'We know what we are, but know not what we may be.' Mental health professionals cannot sit around and languish in the days of doing their prized efforts without modern-day AI. AI is here. AI is advancing. Rapidly. Mental health professionals might know what they are today, but that's not sufficient. They need to be looking ahead to what they will be. The future, entailing advanced AI, shall become an integral part of their world. To be, or not to be.
CBS News
29 minutes ago
- CBS News
Senate approves $9 billion in cuts to foreign aid, public media funding
Washington — The Senate passed President Trump's request to rescind $9 billion in foreign aid and public broadcasting funding early Thursday, culminating an hours-long "vote-a-rama" and sending it back to the House ahead of a Friday deadline. In a 51-48 vote, Republicans Susan Collins, of Maine, and Lisa Murkowski, of Alaska, joined all Democrats in opposing the package. Vice President JD Vance, who cast two tie-breaking votes Tuesday for the measure to clear procedural hurdles, was not needed for final passage. Democratic Sen. Tina Smith of Minnesota was hospitalized and missed the vote. Both chambers need to approve the request before it expires at the end of the week, or the funds will have to be spent as lawmakers previously intended. The House approved the original $9.4 billion rescissions request last month, but it has faced pushback in the Senate, where some Republicans opposed slashing global health assistance and funding for local radio and television stations. The Senate held a lengthy vote series beginning Wednesday afternoon, rejecting dozens of amendments on retaining international aid and sparing public broadcasting from cuts. The Senate's version targets roughly $8 billion for foreign assistance programs, including the United States Agency for International Development, or USAID. The package also includes about $1 billion in cuts for the Corporation for Public Broadcasting, which supports public radio and television stations, including NPR and PBS. Senate Republicans met with Mr. Trump's budget director, Russell Vought, on Tuesday as GOP leaders worked to get holdouts on board ahead of the procedural votes later in the day. Vought left the meeting saying there would be a substitute amendment that would eliminate $400 million in cuts to an AIDS prevention program, one of the main concerns of Republican Sen. Susan Collins of Maine. Senate Majority Leader John Thune, a South Dakota Republican, said he hoped the House would accept the "small modification." When asked about the $400 million change, House Speaker Mike Johnson, a Louisiana Republican, told reporters, "we wanted them to pass it unaltered like we did." "We need to claw back funding, and we'll do as much as we're able," Johnson added. But the change did not satisfy Collins and Murkowski. The holdouts said the administration's request lacks details about how the cuts will be implemented. "To carry out our Constitutional responsibility, we should know exactly what programs are affected and the consequences of rescissions," Collins said in a statement Tuesday. In a floor speech ahead of the procedural votes, Murkowski also said Congress should not give up its budget oversight. "I don't want us to go from one reconciliation bill to a rescissions package to another rescissions package to a reconciliation package to a continuing resolution," she said. "We're lawmakers. We should be legislating. What we're getting now is a direction from the White House and being told, 'This is the priority, we want you to execute on it, we'll be back with you with another round.' I don't accept that." Cuts to local radio and television stations, especially in rural areas where they are critical for communicating emergency messages, was another point of contention in the Senate. Republican Sen. Mike Rounds of South Dakota, who had concerns about the cuts, said funding would be reallocated from climate funds to keep stations in tribal areas operating "without interruption." Republican Sen. Thom Tillis of North Carolina, who voted for the package, said he expected that Congress would later have to try to fix some of the cuts once they determine the impacts. "I suspect we're going to find out there are some things that we're going to regret," he said Wednesday on the Senate floor. "I suspect that when we do we'll have to come back and fix it, similar to what I'm trying to do with the bill I voted against a couple of weeks ago — the so-called big, beautiful bill, that I think we're going to have to go back and work on."
