Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others.

ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer.

It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engine, crawling those published catalogs.

The ARD specification is available now. Organizations are invited to publish their own catalogs using the quickstart guide. After this, they are able to join the community and participate in the evolution of ARD.

This article first appeared on InfoWorld.

Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others.

ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer.

It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engine, crawling those published catalogs.

The ARD specification is available now. Organizations are invited to publish their own catalogs using the quickstart guide. After this, they are able to join the community and participate in the evolution of ARD.

With the market capitalization of AI companies soaring, US Senator Bernie Sanders is looking to give the American people a piece of the action.

The veteran senator for Vermont has introduced the American AI Sovereign Wealth Fund Bill, aiming to give the public a 50 percent ownership in the largest AI companies in the US. It’s a timely move with both OpenAI and Anthropic preparing for the imminent IPOs.

Sanders is not the only one pondering such a move. As the bill notes, OpenAI has proposed the creation of a “Public Wealth Fund” giving citizens a stake in “AI-driven economic growth,” while Anthropic has proposed a sovereign wealth fund to “shape the sector’s behavior.” President Trump’s advisors are also contemplating the possibility of the government grabbing a stake in major AI corporations, according to media reports.

Sovereign wealth funds are not a new idea: Many administrations across the world have implemented them, notably Norway which has about $2 trillion in its wealth fund.

Sanders’ proposal would give the public a direct ownership stake in the largest AI companies, but, he said, it wasn’t just about the wealth. “The foundation of AI is based on the collective knowledge of humanity and the creative work of tens of millions of people. The American people must have the ability to slow it down and make sure that AI benefits humanity, not just the richest people on the planet. That’s precisely what this legislation does,” he said.

An IT executive changing jobs usually attracts little attention outside a narrow group of people, but Noam Shazeer’s move from Google to OpenAI is as momentous as any high-value soccer transfer.

He announced the news in a post on X: “I’m excited to share that I’ll be joining OpenAI and look forward to working with the exceptional team there.”

Shazeer initially achieved fame as one of the eight co-authors of the influential AI paper Attention Is All You Need, published when he was working at Google Brain. He is also one of the creators of the transformer technology that lies at the heart of modern AI models.

He left Google when the company failed to back his chatbot Meena and was tempted back when Google subsequently bought the company he founded, Character.AI, for $2.7 billion. That company achieved notoriety when it was sued by a grieving mother, who alleged that a Character.AI chatbot had contributed to her son’s death by suicide. The company subsequently settling out of court.

Shazeer has since been working as the co-lead on Google’s Gemini project. It’s not clear what role he will play at OpenAI, but hiring someone with his background shortly before the company’s IPO could be an attractive move for investors.

This article first appeared on Computerworld.

An IT executive changing jobs usually attracts little attention outside a narrow group of people, but Noam Shazeer’s move from Google to OpenAI is as momentous as any high-value soccer transfer.

He announced the news in a post on X: “I’m excited to share that I’ll be joining OpenAI and look forward to working with the exceptional team there.”

Shazeer initially achieved fame as one of the eight co-authors of the influential AI paper Attention Is All You Need, published when he was working at Google Brain. He is also one of the creators of the transformer technology that lies at the heart of modern AI models.

He left Google when the company failed to back his chatbot Meena and was tempted back when Google subsequently bought the company he founded, Character.AI, for $2.7 billion. That company achieved notoriety when it was sued by a grieving mother, who alleged that a Character.AI chatbot had contributed to her son’s death by suicide. The company subsequently settling out of court.

Shazeer has since been working as the co-lead on Google’s Gemini project. It’s not clear what role he will play at OpenAI, but hiring someone with his background shortly before the company’s IPO could be an attractive move for investors.

Google, Microsoft, OpenAI, and others want to help enterprises demonstrate that their AI applications are behaving themselves through the creation of a new foundation.

The Appia Foundation will, it explained rather impenetrably, “establish modular specifications that provide a connecting layer to bridge foundational global standards with practical, trusted assessments across the global AI value chain.”

Those specifications will help AI users ascertain whether the systems they are using meet all the obligations that apply to them in the form of standards and regulations, it said. It’s a challenging task with so much regional variation in requirements, and where the EU, for example, is more tightly controlled than the US.

The Foundation has established a set of criteria to demonstrate conformity with what is expected. There are two layers: the Requirements and Guidance layers will help users determine what is actually required, while the Assessment Enablement layer will look at how those requirements are evaluated.

Appia stressed that what it is offering are not standards — which are set by recognized international bodies such as ISO/IEC — but a means of assessing what those standards mean and how they can be used by organizations. However, the Foundation said that some of the criteria that it is introducing may become standards themselves after a period of time.

The Appia Foundation is hosted by the Linux Foundation’s Joint Development Foundation, and its other members include Arm, Ericsson, Mastercard, Mitsubishi Electric, Omron, Schneider Electric, and Siemens. It is also looking to bring academics and government into the fold, so that it can establish an advisory board.

This article first appeared on CIO.

Google, Microsoft, OpenAI, and others want to help enterprises demonstrate that their AI applications are behaving themselves through the creation of a new foundation.

The Appia Foundation will, it explained rather impenetrably, “establish modular specifications that provide a connecting layer to bridge foundational global standards with practical, trusted assessments across the global AI value chain.”

Those specifications will help AI users ascertain whether the systems they are using meet all the obligations that apply to them in the form of standards and regulations, it said. It’s a challenging task with so much regional variation in requirements, and where the EU, for example, is more tightly controlled than the US.

The Foundation has established a set of criteria to demonstrate conformity with what is expected. There are two layers: the Requirements and Guidance layers will help users determine what is actually required, while the Assessment Enablement layer will look at how those requirements are evaluated.

Appia stressed that what it is offering are not standards — which are set by recognized international bodies such as ISO/IEC — but a means of assessing what those standards mean and how they can be used by organizations. However, the Foundation said that some of the criteria that it is introducing may become standards themselves after a period of time.

The Appia Foundation is hosted by the Linux Foundation’s Joint Development Foundation, and its other members include Arm, Ericsson, Mastercard, Mitsubishi Electric, Omron, Schneider Electric, and Siemens. It is also looking to bring academics and government into the fold, so that it can establish an advisory board.

This article first appeared on CIO.

OpenAI has introduced spend controls and enhanced usage analytics for ChatGPT Enterprise to enable organizations to monitor AI adoption, track consumption across teams, and set budgets for AI usage. But, analysts cautioned, it still can’t show how those costs lead to business benefits.

The new features provide administrators with centralized dashboards showing how ChatGPT is being used across an organization, enabling them to understand adoption patterns, and manage AI costs by setting budgets and tracking spending.

“The Global Admin Console brings ChatGPT and Codex credit usage into one view, so admins can see a more granular breakdown of credit consumption across users, products, and models — helping them understand where spend is coming from and how it maps to actual credit usage,” OpenAI said.

A shift toward AI cost governance

The introduction of budgeting and usage analytics reflects a broader change in enterprise priorities, according to Biswajeet Mahapatra, principal analyst at Forrester.

“Enterprises are clearly shifting from adoption-led enthusiasm to cost and value governance, but this is a natural maturity transition rather than a pullback,” Mahapatra said. “AI is no longer an adoption problem but a measurement and credibility problem, with productivity gains present but fragmented and hard to tie to financial outcomes.”

As AI expands across business units, spending becomes distributed across teams, tools and experiments, making visibility and governance increasingly important, he said.

“Budgeting, usage visibility and spend controls are therefore becoming foundational, not just operational concerns, as they enable alignment between technology usage and business outcomes,” Mahapatra added.

OpenAI is addressing those needs with its new dashboards and spending controls address, he said.

Agent sprawl will add to cost control challenges

Anushree Verma, senior director analyst at Gartner, said AI cost governance is becoming a focus for enterprises, as fragmented pricing models, vendor-defined consumption units and inconsistent pricing structures make it difficult for organizations to predict AI costs.

She expects the challenge to intensify over the next few years as enterprises scale up their use of AI.

“By 2028, an average global Fortune 500 enterprise will have over 150,000 agents in use, up from less than 15 in 2025, generating significant agent sprawl, IT complexity and management challenges,” she said.

Against that backdrop, OpenAI’s new administrative capabilities provide organizations with tools to monitor organizational usage and spending as AI deployments become larger and more distributed.

Measuring AI value beyond consumption

While OpenAI’s analytics emphasize adoption patterns and usage visibility, analysts say enterprises will ultimately need to connect AI consumption with business outcomes.

“Token consumption alone is insufficient because it measures activity rather than impact,” Mahapatra said.

Instead, organizations should evaluate AI initiatives using business metrics such as revenue growth, cost reduction and risk mitigation alongside operational indicators including productivity improvements and quality gains.

Verma said traditional cloud FinOps practices are also evolving to accommodate AI’s usage-based economics.

“Traditional FinOps practices were built around predictable, centralized cloud environments and are insufficient for handling unpredictable AI consumption metrics, such as token usage, LLM requests and GPU hours,” she said.

She added that real-time tracking is becoming increasingly important as multiagent systems scale, because misconfigurations can cause AI costs to rise rapidly across interconnected environments.

This article first appeared on CIO.

As AI shifts from output-generating large language models (LLMs) to armies of agents taking actions on their own, there is a growing threat that failures could affect system reliability.

Temporal, a Bellevue, WA firm founded in 2019, hopes to solve that problem by stabilizing AI and long-running computing processes through “durable execution,” a technology that reliably resurrects failed computing processes on other hosts. If a machine crashes in the middle of agentic AI transactions, the company resurrects the function on a different host so it can continue exactly where it left off. Temporal says its durable execution process comes with a 100% durability guarantee.

The company was solving these kinds of distributed-systems problems long before the generative AI (genAI) rush, and today it powers infrastructure from Coinbase to Airbnb to OpenAI.

For IT decision-makers, Temporal’s offerings promise to bring reliability to AI operations — especially in regulated industries. Co-founder Samar Abbas spoke recently with Computerworld about his company’s technology, AI reliability, execution and what IT leaders need to keep in mind when evaluating and deploying agentic AI.

AWS

How did Temporal come to be a backbone powering AI system? “OpenAI recently announced we power many of their products underneath the cover — that’s where Temporal comes in.

“A couple of years ago, as LLMs got smarter, use cases were about generating outputs. Now, the industry is moving into agentic solutions, where AI isn’t just generating outputs, it’s taking actions. The longer these systems run, the more tools they invoke and the more value they create. 

“They’re evolving into a whole army of agents coordinating to get complex tasks done. This starts to look like distributed systems. We become that execution authority, the reliability backbone for those agentic systems to get their tasks done from A to Z.”

What is Temporal doing in the backend to keep them stable? “It’s a new paradigm for building applications, we call that durable execution. If you write a function and a failure happens, you lose all of your state. When a machine crashes, we maintain the state. We seamlessly resurrect that function on a different host and continue executing exactly where it left off, without you as a developer writing a single line of code. 

“It’s not just a cache, it’s an operating system guaranteeing execution in the presence of failure. We provide 100% durability guarantees behind it. People can use it for mission-critical use cases like transcribing doctor conversations, where even losing one [word] has big implications.”

Did this challenge exist before AI? “My co-founder and I have been at it pretty much our entire careers, spanning almost three decades. We’ve been solving this problem of distributed systems, at Amazon, Microsoft and then Uber, before starting Temporal. We are definitely pre-AI. 

“As more workloads move to cloud and become distributed, it introduces this class of failures. This is the fifth time I’m building this system. I built simple workflow, then [a] durable task framework at Azure, then Cadence at Uber, now Temporal. The company is roughly seven years old, but the code base is over 10 years old, because we started at Uber and it’s been running in production ever since.”

And now you tackle that same problem at AI scale? “The funny thing is, especially with AI, that same problem is now at such a massive scale, it’s gone on steroids, with agentic systems coming online and doing real work. We’ve been powering every Coinbase transaction, every Snap story, every Airbnb booking, every Yum Brands order —  KFC, Pizza Hut, Taco Bell.

“A couple of years ago, it looked like we’d over-invested in the scale and reliability of the platform. Then AI showed up in such a big way that now I’m asking the team: how do we scale another 10x, 100x, even 1000x? These agents are hitting the market in a way we couldn’t have imagined.”

Can you give a concrete example of that 100% guarantee? “When you’re moving money — debit or credit — imagine a failure happens after you’ve debited an account. Temporal guarantees execution of the entire function from start to finish, in the presence of all sorts of failures. At the end of the day, we’re in the business of selling reliability. 

“Developers today spend 80% of their time building resiliency into their systems by working with low-level primitives like queues, databases, retry mechanisms and durable timers, and stitching them together. What we provide eliminates that. And that reliability is exactly what’s becoming critical as enterprises move agents into production.”

Why should CIOs and other IT leaders care about AI reliability? “These agents are now real, no longer prototypes or [proofs of concept]. They’re doing meaningful work in those organizations. Reliability and durability weren’t on the radar for CIOs 12 months ago. Now, it’s the blocker. Especially in regulated industries, if a transaction gets left in the middle, it has real regulatory concerns. 

“That’s what’s holding them back from transitioning into the AI stage. It comes up in pretty much every conversation, in the first 10 minutes I’m talking to a CIO.”

CW: Where do the hardest problems sit today? “Security, governance, identity, authorization, authentication. This is where majority of the problems exist, and the ecosystem is so immature. Even things like MCP, I see organizations struggling to implement MCPs to build these agents. This is an area ripe for innovation and a lot of work, including here at Temporal.”

What skills should developers and CIOs build for the AI age? “The biggest skill now is that developers need to be more product focused. Product-minded engineers are the ones who will thrive, because the whole software development flow, writing code by hand, debugging by hand, testing it, is going to look completely different. The job of an engineer is now building the system that builds the system. 

“Two things are happening in this AI world. First, a lot more engineers are coming into the fold, because companies like Replit or Lovable are making software development approachable for people who haven’t been professionally trained. Second, there’s going to be a lot more applications written than ever before. The problem space is shifting toward how we run all those applications safely and reliably, with all the guardrails. That’s where the industry is headed.”