Models & Labs · United States

IBM puts agentic workloads at the heart of its AI operating model

Credited to SiliconANGLE · siliconangle.com · 3h ago

Photograph · SiliconANGLE

Agentic workloads present a new operating challenge for enterprises putting artificial intelligence into production. As autonomous systems take on ongoing responsibilities, organizations must support decisions and actions across their data, applications and infrastructure.

IBM Corp.’s approach draws on the experiences of clients scaling AI across their organizations. That work helped identify the requirements underpinning its AI operating model, according to Ritika Gunnar (pictured), chief performance marketing officer of IBM.

“As you’re more successful in using AI, especially as AI is taking action, you now have workloads that are continuously being utilized,” she said. “You are incented to be always on because those agents are always taking action for you.”

Gunnar spoke with theCUBE Research’s Dave Vellante and John Furrier for IBM’s AI Operating Model interview series in an exclusive interview on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed enterprise integration, data governance, human oversight and evaluating AI against business outcomes in preparation for IBM TechXchange 2026. (* Disclosure below.)

Connecting agentic workloads across the enterprise

IBM’s AI operating model brings together four components that enterprises may already obtain from different providers. Applying them means accounting for established applications and business processes alongside emerging AI systems, Gunnar explained.

“You need to be able to integrate with your existing systems,” she said. “People aren’t going to run away from their SAP systems that exist, from their Salesforce systems that exist. What does it mean to integrate with those and have workloads that are representative of your past and your future together?”

The components have distinct roles: Data supplies current enterprise knowledge, workloads encompass applications and workflows, automation supports oversight and optimization, and hybrid cloud accommodates distributed operations. Their effectiveness depends on how they function together across agentic workloads, according to Gunnar.

“If you have your data and that data is feeding your systems, but you’re not really taking care of automation in a way where you have complete observability of all of your workloads with that AI infused, then you’re not necessarily having an optimal way of picking the right model or picking the right agents for those kinds of workloads,” she said.

Bringing governance and human judgment into decisions

The information supporting an agent’s decision may span operational systems, edge devices and context generated by other agents. Providing governed context requires enterprises to consider both the completeness of that information and how permissions travel with it, Gunnar explained.

“Our belief is if you can take data and if you can shift left a lot of the governance — a lot of the who has the right access to the right data at the right time — it becomes important because you flow that data in real time with the context and provenance of who actually has ownership of that data,” she said. “Therefore, the agents make much better decisions.”

Enterprises also depend on knowledge employees develop through experience rather than explicitly recording it in their systems. Prompts and other context can help convey that judgment, but capturing it remains an ongoing challenge, Gunnar pointed out.

“When you think about workloads today … there is a lot of it that will be autonomous in nature,” she said. “But in most cases, if you’re talking about your essential systems, you do have humans in the loop to be able to bring some of that tacit knowledge in place where the AI has not yet matured to be able to even get there.”

Evaluating performance against business outcomes

Agent activity generates logs of decisions and actions, while prompts record the instructions supplied to those systems. Together, that information supports an ongoing feedback loop between enterprise data and AI workloads, Gunnar explained.

“You can actually have multiple different permutations of that agent making a decision and having another set of eval loops actually doing an evaluation of, ‘Did that agent actually make the decisions?’” she said. “Would there have been a more performant way, a more accurate way, a more cost-effective way to be able to do that?”

Performance encompasses accuracy, timeliness and cost, with the intended business result guiding how those measures are assessed. Putting AI into production therefore requires organizations to examine the work itself alongside the technology supporting it, according to Gunnar.

“Make sure that you have all of these elements and that you’re really thinking about the outcomes, and you’re willing to start with what it means to take a look at the business process transformation,” she said. “That’s really where it’s at.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of IBM’s AI Operating Model interview series:

(* Disclosure: TheCUBE is a paid media partner for IBM’s AI Operating Model interview series event. Neither IBM, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.

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Models & LabsUnited States

MeitY unveils indigenous AI Models EKA-1, Dhīr 2B and 5B under IndiaAI mission

Credited to Mint · livemint.com4h ago

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Photograph · Mint

New Delhi [India], November 3 (ANI): Ministry of Electronics and Information Technology (MeitY) has unveiled three indigenous artificial intelligence foundation models, EKA-1, Dhīr 2B and Dhīr 5B, developed by domestic companies with support under the IndiaAI Mission, with the government highlighting their potential for specialised applications and deployment in secure, offline environments.

MeitY Secretary S. Krishnan, while congratulating the teams behind the models, said the initiative would strengthen India's strategic autonomy in artificial intelligence and expand the availability of models suited to domestic requirements.

The models have been developed by Bengaluru-based Socket AI and ZenteiQ under the Foundation Model initiative of the IndiaAI Mission.

EKA-1, developed by Socket AI, is a 26-billion-parameter foundation model designed for mathematics, coding and reasoning. It has been trained on 7.5 trillion tokens and supports more than 100 Indian and international languages, as well as programming languages. The model is designed to handle multi-step tasks, execute code and operate in on-premises, private cloud and air-gapped environments.

Dhīr 2B and Dhīr 5B, developed by ZenteiQ, are mixture-of-experts foundation models trained from scratch using Google Cloud Tensor Processing Units (TPUs). The models support English and 10 Indian languages and can run directly on tested mobile hardware without an internet connection.

ZenteiQ said it was releasing the model weights under the Apache 2.0 licence, along with technical documentation and evaluation tools, through AIKosh.

Krishnan said the models had specific applications beyond general-purpose use, including mathematics, coding, scientific research and cybersecurity. He particularly highlighted their ability to operate in air-gapped environments, which could support wider adoption of enterprise artificial intelligence.

"These models can be deployed on air-gapped environments, which means enterprise-based artificial intelligence, which thus far hasn't really taken off, would be possible to do a lot more of that in the country using models like this," he said.

He added that smaller models could improve productivity and efficiency across different applications and that the government would continue supporting their refinement and development.

The models are accessible through AIKosh for researchers, students, innovators, enterprises and the general public, making them a wider national resource, Krishnan said. (ANI)

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Snowflake expands agentic AI platform with business context and governance tools

Credited to SiliconANGLE · siliconangle.com · 5h ago

Snowflake Inc. today announced a sweeping collection of artificial intelligence capabilities designed to bring business context, governance and autonomous development into the same environment, as enterprises increasingly hand over software engineering and everyday operations to AI agents.

The announcements, during the company’s Expedition 2026 virtual conference, include a new Business Ontology framework, expanded agent governance through Cortex AI Gateway, and autonomous data engineering capabilities that allow AI agents to build, monitor and troubleshoot data pipelines with less human intervention.

The company is betting that more coders and engineers will use AI throughout the software development lifecycle as creation moves even faster, while other back-end departments begin to adopt agents in their day-to-day work. For example, sales operations are creating agents that can analyze sales risks and prepare customer outreach without writing code.

“AI is industrializing software creation. Actually, it’s industrializing intelligence,” Chief Executive Sridhar Ramaswamy said. “What once took months can now take hours, days, or even minutes.”

Ramaswamy noted that while AI is changing the economics of software development, as agents take over tasks usually relegated to specialized developers, business users become developers. This is changing the competitive landscape, meaning that the advantage no longer goes to specialists, but to proprietary knowledge.

Snowflake is taking the field by helping ordinary AI models understand and contextualize that business knowledge. It does this with Business Ontology, an extension of its existing semantic views that lets organizations establish approved definitions used across the organization. Without a standard definition, two agents could respond to the same business-related question with incompatible answers, even when both agents have accurate underlying data.

Using information from Business Ontology, Snowflake CoCo, the coding agent for data teams, and Snowflake CoWork, the personal work agent for knowledge workers, use these approved business concepts to ground their responses.

This new feature is currently in private preview, but is headed to public preview soon.

Governing agents like employees

Cortex AI Gateway arrives to become a single front door for agents within an organization. It is not entirely new; Snowflake introduced it in July 2026 as a centralized way to govern and monitor agents.

Today’s announcement expands its capabilities rather than introducing the gateway for the first time. The new updates include agent abuse detection, observability and tracing, Cortex sandboxes, AI cost governance, dynamic model routing and tools integration using technology from Snowflake’s Natoma acquisition.

“An agent that acts on your business has to be trusted, and it has to know three important things: what the data means, which data to trust, and what it’s allowed to do,” said Ramaswamy.

The platform most importantly applies existing security mechanisms, including data masking and access restrictions, to agent-driven operations while adding capabilities to monitor activity, detect potential abuse and maintain records of what agents do. The information technology and security teams can also apply cost controls, select models based on efficiency or accuracy and restrict which external tools agents can access granularly.

The underlying role of this governance, control and transparency is aligned with giving agents the same sort of role observation granted to employees. Treating them similarly in terms of autonomy.

Snowflake’s push comes amid a broader industry race to establish the control layer for enterprise AI agents. SAP has been expanding its Autonomous Enterprise architecture and governed Joule agents, while Salesforce has introduced its Enterprise AI Harness and AI Control Plane to secure autonomous workflows. Snowflake is approaching the same problem from the data platform side, seeking to become the foundation through which agents access trusted business information and operate under enterprise policies.

Training smaller models for specialized tasks

Snowflake also announced the public preview of Cortex Training, which will allow customers to tailor open-weight AI models with their own proprietary information.

The service is designed to make it simpler to create specialized models using post-training without needing vast arrays of graphics processing units. Snowflake lets them train models without managing the underlying infrastructure.

“Specialized, smaller models that come with more efficient responses, faster responses, and frequently cost benefits in terms of tokens,” Executive Vice President of Product Christian Kleinerman said.

Many enterprise companies will require sovereign control of their own AI models as increasing restrictions on foreign open-weight AI or regulations on handling sensitive information. It’s easier to audit what passes through an AI model on your own hardware than in a third-party vendor’s cloud.

Separately, Snowflake announced an expanded partnership with Oracle Corp. to make its platform available as a first-party service on Oracle Cloud Infrastructure. The integration will give customers easier access to Snowflake’s analytics and AI capabilities while allowing them to work directly through Oracle databases and applications.

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