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A standard, open framework for building AI agents is coming from Cisco, LangChain and Galileo

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More One goal for an agentic future is for AI agents from different organizations to freely and seamlessly talk to one another. But getting to that point requires interoperability, and these agents may have been built with […]

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One goal for an agentic future is for AI agents from different organizations to freely and seamlessly talk to one another. But getting to that point requires interoperability, and these agents may have been built with different LLMs, data frameworks and code.

To achieve interoperability, developers of these agents must agree on how they can communicate with each other. This is a challenging task. 

A group of companies, including Cisco, LangChain, LlamaIndex, Galileo and Glean, have now created AGNTCY, an open-source collective with the goal of creating an industry-standard agent interoperability language. AGNTCY aims to make it easy for any AI agent to communicate and exchange data with another.

Uniting AI Agents

“Just like when the cloud and the internet came about and accelerated applications and all social interactions at a global scale, we want to build the Internet of Agents that accelerate all of human work at a global scale,” said Vijoy Pandey, head of Outshift by Cisco, Cisco’s incubation arm, in an interview with VentureBeat. 

Pandey likened AGNTCY to the advent of the Transmission Control Protocol/Internet Protocol (TCP/IP) and the domain name system (DNS), which helped organize the internet and allowed for interconnections between different computer systems. 

“The way we are thinking about this problem is that the original internet allowed for humans and servers and web farms to all come together,” he said. “This is the Internet of Agents, and the only way to do that is to make it open and interoperable.”

Cisco, LangChain and Galileo will act as AGNTCY’s core maintainers, with Glean and LlamaIndex as contributors. However, this structure may change as the collective adds more members. 

Standardizing a fast-moving industry

AI agents cannot be islands. To reach their full potential, they must be able to communicate with other agents that lie outside of an enterprise’s network. This is where interoperability comes in.

Setting standards in traditional industries is challenging enough; it becomes even more difficult for technology like AI, where upgrades and model changes occur every few months. However, this is not the first time a standard has been proposed for generative AI. 

LangChain, one of AGNTCY’s core members, has its own protocol for working with agents built on frameworks other than LangChain. The Agent Protocol, launched in November last year, allows LangChain agents to talk to agents created with AutoGen, CrewAI or any other framework.

Meanwhile, Anthropic announced its Model Context Protocol (MCP) in November. This protocol aims to standardize how models and AI tools connect to data sources. But while many developers have embraced MCP, it’s not exactly a standard just yet. 

Yash Sheth, cofounder of AI evaluation platform Galileo, said standardization “is critical.”

“Standardization is needed, in fact, it will drive increased velocity for agentic adoption. Today, teams are building in silos, having to figure out how to develop their own infrastructure components from scratch,” Sheth said in an email. “Standardization of multi-agentic systems can only happen if these agents powered by non-deterministic models have a strong anchor in measuring and reporting their performance, accuracy and reliability.”

Sheth admits that making AI agents interoperable can be complex. AGNTCY “wants to encourage developers to extend these specs, APIs and tools to suit their needs instead of reinventing the wheel, which will be crucial to achieving standardization.”

LangChain CEO Harrison Chase said in a separate conversation that creating a standard is not impossible, especially now that it’s easier to build the agents themselves. 

“Building agents is already possible, and being done. Replit, Klarna, LinkedIn, Uber, Appfolio and many others have all already done this. Agents aren’t a thing of the future, they are now. Now that we know how to build agents, the next step is to allow them to connect to each other. That is what a standard agent protocol will help enable,” Chase said. 

A platform and a language all at once 

Pandey envisions AGNTCY as more than just a set of codes for agents. It will also allow customers to discover agents from different developers who run the AGNTCY standard. 

“Customers can stitch together all these agents on the AGNTCY platform so they can discover, compose, deploy and evaluate as they build their workflows,” Pandey said.  

AGNTCY still needs to recruit more AI players to add new agents to the platform and gain momentum as a standard. After all, for something to become an industry standard, there needs to be mass adoption, to prevent the establishment of too many competing standards. 

That’s where projects like AGNTCY face an uphill battle. Pandey said the collective has been speaking with many other industry players, and they want to get as many viewpoints as possible while developing the platform. That will take time. 

In the meantime, enterprises continue to experiment and even deploy AI agents. Maybe in the future, these will all be able to speak to each other. 

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SolarWinds buys Squadcast to speed incident response

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Basin Electric urges Congress to support clean energy tax credits

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USA Crude Oil Inventories Rise WoW

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Stranded energy assets put UK on course for $141bn loss, says study

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DOE approves LNG export permit extension for Golden Pass

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Storegga applies to build Speyside green hydrogen hub

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Data center vacancies hit historic lows despite record construction

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AI driving a 165% rise in data center power demand by 2030

Goldman Sachs Research estimates the power usage by the global data center market to be around 55 gigawatts, which breaks down as 54% for cloud computing workloads, 32% for traditional line of business workloads and 14% for AI. By 2027, that number jumps to 84 GW, with AI growing to 27% of the overall market, cloud dropping to 50%, and traditional workloads falling to 23%, Schneider stated. Goldman Sachs Research estimates that there will be around 122 GW of data center capacity online by the end of 2030, and the density of power use in data centers is likely to grow as well, from 162 kilowatts per square foot to 176 KW per square foot in 2027, thanks to AI, Schneider stated.  “Data center supply — specifically the rate at which incremental supply is built — has been constrained over the past 18 months,” Schneider wrote. These constraints have arisen from the inability of utilities to expand transmission capacity because of permitting delays, supply chain bottlenecks, and infrastructure that is both costly and time-intensive to upgrade. The result is that due to power demand from data centers, there will need to be additional utility investment, to the tune of about $720 billion of grid spending through 2030. And then they are subject to the pace of public utilities, which move much slower than hyperscalers. “These transmission projects can take several years to permit, and then several more to build, creating another potential bottleneck for data center growth if the regions are not proactive about this given the lead time,” Schneider wrote.

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Top data storage certifications to sharpen your skills

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Netskope expands SASE footprint, bolsters AI and automation

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Inside the Nuclear Race for Data Center Energy with Aalo Atomics CEO Matt Loszak

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Does It Matter If Microsoft Is Cancelling AI Data Center Leases?

Strategic Reallocation: Microsoft is a major owner and operator of data centers and might be reallocating resources to in-house infrastructure rather than leased spaces. Supply Chain Delays: TD Cowen noted that Microsoft used power and facility delays as justifications for voiding agreements, a tactic previously employed by Meta. Oversupply Issues: Analysts at TD Cowen speculate that Microsoft may have overestimated AI demand, leading to an excess in capacity. As it is all speculation, it could simply be that the latest information has driven Microsoft to reevaluate demand and move to more closely align projected supply with projected demand. Microsoft has reiterated their commitment to spend $80 billion on AI in the coming year. Reallocating this spending internally or wit a different set of partners remains on the table. And when you put the TD Cowen report that Microsoft has cancelled leases for “a couple hundred megawatts” into context with Microsoft’s overall leased power, which is estimated at around 20 GW, you see that more than 98% of their energy commitment remains unchanged. Investment Markets Might See the Biggest Hits Microsoft’s retreat has had ripple effects on the stock market, particularly among energy and infrastructure companies. European firms like Schneider Electric and Siemens Energy experienced a decline in stock value, indicating fears that major AI companies might scale back energy-intensive data center investments. However, at press time we have not seen any other indicators that this is an issue as despite these concerns about potential AI overcapacity, major tech firms continue to invest heavily in AI infrastructure:         Amazon: Pledged $100 billion towards AI data centers.         Alphabet (Google): Committed $75 billion.         Meta (Facebook): Planning to spend up to $65 billion.         Alibaba: Announced a $53 billion investment over the next three years. If we see a rush of announcements

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Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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