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AI agents drive up telemetry costs, complicate network observability

“A production agent emits 4-5x more data as telemetry,” said Kamal Hathi, senior vice president and general manager of Splunk at Cisco, in an interview with Network World. Beyond traditional application traces, agents generate telemetry about workflows, tool calls, retrieved context, model interactions, and authentication. As agents interact with other agents, the volume of telemetry […]

“A production agent emits 4-5x more data as telemetry,” said Kamal Hathi, senior vice president and general manager of Splunk at Cisco, in an interview with Network World.

Beyond traditional application traces, agents generate telemetry about workflows, tool calls, retrieved context, model interactions, and authentication. As agents interact with other agents, the volume of telemetry generated during multi-agent sessions can increase exponentially, Hathi explained.

“AI agents are both a source and consumer of machine data,” said Greg Leffler, director of developer evangelism at Splunk. “So, this two-fold demand creates an influx of data volume fast, contributing to unpredictable costs.”

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AI agents drive up telemetry costs, complicate network observability

“A production agent emits 4-5x more data as telemetry,” said Kamal Hathi, senior vice president and general manager of Splunk at Cisco, in an interview with Network World. Beyond traditional application traces, agents generate telemetry about workflows, tool calls, retrieved context, model interactions, and authentication. As agents interact with other

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SolarWinds launches Avior AI agents to automate network operations

The first Avior offering is a network engineering agent that can automate alert management, configuration automation, compliance checks, and incident remediation. “We started there because [the network is] kind of our bread and butter,” Childress explains. “Many of our customers, a large portion of our customers, are on our network

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Energy Department Announces New Investments to Strengthen America’s Power Grid and Lower Electricity Costs

WASHINGTON— The U.S. Department of Energy (DOE) today announced approximately $967 million in additional funding through its Grid Resilience State and Tribal Formula Grant program to modernize critical energy infrastructure, lower electricity costs and strengthen America’s power grid. The funding will support locally driven projects across states, territories, and tribes to upgrade critical infrastructure, prevent power disruptions, and meet growing electricity demand while helping lower costs for American families and businesses. The investment advances President Trump’s American energy dominance agenda. Projects funded through the program will help states, territories, and tribes invest in infrastructure improvements that protect against disruptive events, improve system performance, and ensure communities and businesses have dependable power when they need it most. “Lowering costs and unleashing prosperity are top priorities for President Trump,” said U.S. Secretary of Energy Chris Wright. “These commonsense investments provide resources directly to states, territories, and tribes to lower electricity costs and strengthen grid reliability for American businesses and families.” “A strong and dependable grid is the backbone of American economic prosperity,” said Office of Electricity Assistant Secretary Katie Jereza. “This funding will help Americans across the country, including our tribal partners, optimize their electricity systems and prepare for future growth while strengthening energy security and deploying technologies that can lower the long-term cost of delivering reliable power.” States, tribes, and U.S. territories can view their funding allocations and application instructions here.

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Eni, EGPC launch Meleiha Phase 2 gas project in Egypt

Agiba Petroleum Co.—a 50-50 joint venture of Eni SPA subsidiary IEOC Production BV and Egyptian General Petroleum Corp. (EGPC)—has initiated startup activities for its Meleiha Phase 2 project in Egypt’s Western Desert, advancing development of a gas hub in the Merged Meleiha concession. Part of the operator’s development of a new gas hub within the Merged Meleiha concession and designed to increase processing capacity for from the area’s natural gas fields to support growth of Egypt’s domestic output, the planned installation will be equipped to process up to 100 MMcfd of gas production, Eni said in a release Oct. 20. Planned infrastructure at the site includes development wells, gathering systems, trunklines, and a dedicated gas-export pipeline connecting new production to Egypt’s existing gas network. Eni did not reveal a schedule for completing full startup and commissioning activities for Meleiha Phase 2, but—once completed—the company said the project will help enable the rapid monetization of Egypt’s natural resources and enhanced operational efficiency. Western Desert development Meleiha Phase 2 builds on Agiba’s ongoing exploration and development in the Western Desert concession. In April 2022, Eni reported that three discoveries in the Meleiha concessions had been tied into production, adding an estimated 8,500 boe/d. The Nada E Deep 1X well encountered 60 m of net hydrocarbon pay in the Alam El Bueib and Khatatba formations. Meleiha SE Deep 1X found 30 m in the Matruh and Khatatba formations, while Emry Deep 21 encountered 35 m in Alam El Bueib. Including discoveries made in 2021, Eni said the results brought the exploration total to eight wells, with a 75% success rate. Agiba operates the Meleiha concession, in which it holds a 76% interest alongside partner Lukoil PJSC (24%), according to Eni’s website.

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Global LNG market faces demand cuts as Hormuz disruptions strain supply

Prolonged disruptions to LNG shipments through the Strait of Hormuz are putting mounting pressure on global natural gas markets, with limited spare liquefaction capacity leaving demand reductions as a key mechanism for restoring market balance. Despite elevated prices in Europe and the Asia Pacific, alternative suppliers have limited ability to quickly replace lost Gulf exports, raising the prospect of tighter markets extending into 2027. European gas prices remain elevated as the Northern Hemisphere enters winter with unusually low inventories. Dutch TTF natural gas futures traded around €88/MWh (about $29/MMbtu) on Oct. 19, while EU underground storage stood at 74.6% of capacity as of Oct. 18, compared with a 5-year seasonal average of about 89%. German storage was particularly low, at about 61%. The Strait of Hormuz remains a major supply constraint. Since the Middle East conflict began in late February, around 80% of previously available LNG exports from Qatar and the UAE have been sidelined. Combined exports averaged 1.73 billion cu m (bcm)/month during March–September, compared with a 5-year pre-crisis average of 9.37 bcm/month. Renewed attacks on commercial shipping have complicated prospects for a sustained recovery in shipments. An October analysis by the Oxford Institute for Energy Studies (OIES) suggests that prolonged supply disruptions would be absorbed primarily through reduced LNG imports and demand rather than increased production elsewhere. Using an April 2027 restart of Gulf LNG exports as its modeling baseline, OIES estimates that delaying the recovery until October 2027 would create a 49-bcm supply gap in 2027 relative to that baseline. Other exporters would provide only 7.3 bcm of additional supply, leaving 41.7 bcm, or 85% of the shortfall, to be absorbed through lower imports and demand. US LNG exports offer limited additional flexibility US LNG export installations continue operating at high utilization rates, supported by the substantial

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National Fuel plans ’27 spinoff of Appalachian natural gas group

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US waives Russia sanctions as Gulf oil shipments face renewed disruption

President Donald Trump signed an executive order Oct. 16 waiving key sanctions and tariffs under the Graham Act, including duties on Russian goods and penalties for countries that buy Russian oil and gas. The measures were due to take effect within days. Earlier Russia sanctions imposed under previous executive orders remain in place. US Secretary of State Marco Rubio said the administration would use up to 60 days to assess the economic effects. The order specifies no expiration date, and the waiver remains in effect pending further action by the administration. Brent crude futures fell Oct. 19 from a 5-month high as traders weighed the prospect of more Russian supply against continued Middle East risks. December Brent futures traded around $108/bbl in morning dealings, down 1% to 2%. Diesel deal The waiver follows a separate diesel agreement. Trump said Russia would immediately supply more than 300,000 tonnes of diesel to the US and global markets, followed by 500,000 tonnes in November and 1 million tonnes after that. He also cited a further 3 million tonnes, depending on the condition of Russian refineries. Delivery remains uncertain. Ukrainian strikes have damaged Russian refineries, while Moscow’s diesel export restrictions remain partly in place despite easing on Oct. 10. Middle East risks The potential increase in Russian fuel exports comes as shipping disruptions continue to constrain Middle Eastern oil supplies. According to Kpler data, Gulf crude flows fell to an average of about 8.5 million b/d last week, compared with more than 20 million b/d the preceding week. Shipping security has also deteriorated. Kpler reported at least 10 confirmed incidents involving commercial vessels last week. Separately, Iran’s Islamic Revolutionary Guard Corps said a tanker struck a naval mine and exploded in the southern Strait of Hormuz on Oct. 18, according to Iranian media cited

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Energy Department Advances Constitution Pipeline Project with $42 Million in Defense Production Act Funding

WASHINGTON—The U.S. Department of Energy (DOE) announced today that it will use up to $42 million in Defense Production Act (DPA) Title III funding to advance the development of a proposed 125-mile natural gas pipeline from Pennsylvania to New York.  Constitution Pipeline Company, LLC’s project will transport natural gas to markets across the Northeast that currently experience limited access to the low-cost reliable fuel. By leveraging DPA authorities, DOE is helping strengthen the security of the nation’s energy system and defense industrial base. The project will also dramatically lower electricity and home heating costs across the region. “Despite America having an abundant supply of low-cost natural gas, tens of millions of Americans have limited access to this reliable energy source due to a lack of pipeline infrastructure,” said U.S. Secretary of Energy Chris Wright. “President Trump promised to lower energy costs and reverse the energy emergency he inherited that was caused by 17 years of energy subtraction policies. That is why he promised to build the Constitution Pipeline – a project that will transform the lives and economic opportunities for Americans across New York and New England. It will also reduce the risk of blackouts during periods of peak electricity demand and make our nation more secure.” “For too long, families and businesses in New York and other blue states have paid the price for anti-energy policies that kneecap production, block critical infrastructure and drive up costs,” said Secretary of the Interior and Chairman of the National Energy Dominance Council Doug Burgum. “President Trump is reversing that failed approach, choosing abundance over government-imposed scarcity. This historic investment in New York will help deliver the reliable, secure energy Americans deserve while saving families across the Northeast billions of dollars.” This investment supports President Trump’s commitment to ensuring all Americans have access

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AI’s GPU Refresh Cycle Is Becoming a Stranded-Capital Problem

The AI infrastructure buildout is measured in gigawatts and successive generations of accelerators. But as operators bring new compute capacity online, a less visible question is gaining financial urgency: What happens to GPU systems when their first production assignment ends? For hardware that remains functional but no longer meets the performance or efficiency requirements of a leading-edge AI cluster, retirement need not mean obsolescence. Equipment may be redeployed, resold, harvested for components or processed to recover materials. The outcome depends on decisions made long before a system leaves its rack. That was the central argument advanced by Linda Li, chief strategy officer of Re-Teck and its parent company, Li Tong Group, in the latest onsite installment of the DCF Show podcast, hosted by Data Center Frontier Contributing Editor Doug Black at the DCF Trends Summit 2026 in Northern Virginia. For Li, the acceleration of GPU refresh cycles is making lifecycle management a question of capital preservation, facilities design and executive governance—not merely IT asset disposition. Plan for the Exit Before the Equipment Arrives Li draws on more than two decades of her company’s work in reverse supply chains for consumer electronics and telecommunications equipment. Both sectors have managed rapid generational shifts without assuming that every displaced device had exhausted its useful life. “We are dealing with a totally different organism,” she said, describing the combination of rapid accelerator iterations, rising rack power density and increasingly sophisticated thermal requirements. The response, in her view, has two levels. The first is physical infrastructure: facilities designed to accommodate successive generations of servers without extensive reconstruction. Scalable power distribution, adaptable raceways, modular rack systems and high-capacity air and liquid cooling can create room for hardware transitions that may arrive sooner than originally expected. The second is the network of partners and contracts around the

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Beyond Colossus: 5C Advances $575M Memphis AI Factory Expansion

Memphis has emerged as a major center of AI infrastructure development, driven largely by the extraordinary scale of xAI’s Colossus supercomputing operations. But the city’s growing AI computing market extends beyond Elon Musk’s flagship project. At the former headquarters and distribution complex of discount retailer Fred’s, 5C Data Centers is developing another substantial AI computing facility, combining industrial adaptive reuse with a phased expansion strategy and plans for behind-the-meter power generation. On October 6, 2026, a $575 million commercial alteration permit application was filed for the fourth phase of 5C’s MEM01 data center at 4300 Getwell Road. The application covers approximately 504,000 square feet of the property’s 1.1 million-square-foot building, representing the largest construction phase yet proposed at the site. MEM01 is already operating. According to September reporting from The Daily Memphian, 5C had established operations in approximately 200,000 square feet of the former Fred’s property, leaving substantial additional space available for conversion. The latest permit application marks a significant escalation in the scope of the development. The expansion also introduces a major new element to 5C’s power strategy. On October 11, 5C Group Chief Revenue Officer Brad Nimri told The Daily Memphian that the company plans to increase available power at MEM01 from approximately 20 MW to 84 MW over the next six months, using fuel cell technology to supply the additional capacity rather than relying on a corresponding increase in electricity from Memphis Light, Gas and Water (MLGW). Like Colossus, which began with the conversion of a former Electrolux manufacturing facility, 5C’s development reflects the growing role of industrial adaptive reuse in AI infrastructure. The projects differ considerably in scale and their approaches to on-site power generation, but both illustrate the challenge of transforming existing industrial properties into high-density computing environments with substantial electrical requirements. As Memphis considers

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DCF Global: The Nordics’ AI Boom Meets the Power Test

Sustainability Becomes an Infrastructure Requirement Sustainability has long been central to the Nordic data center proposition. Hydropower, nuclear generation, wind resources and relatively cool ambient temperatures provide advantages for operators pursuing lower-carbon operations and efficient facility design. However, the growing concentration of AI infrastructure investment is changing the terms of that discussion. Rather than evaluating sustainability primarily through electricity sourcing or annualized power usage effectiveness, developers increasingly face questions about their contribution to regional energy systems, electricity supply, environmental protection and local economic development. One area where the Nordics offer a particularly developed model is waste-heat reuse. District heating networks are widespread across northern Europe, creating opportunities to recover thermal energy from data centers and distribute it to homes, businesses and other facilities. The approach has a long history in the region, including the Danish and Swedish projects explored by Rich Miller in Data Center Frontier’s earlier coverage of using server heat to warm nearby homes. In Espoo, Finland, atNorth has begun supplying recovered heat from its FIN02 data center to a neighboring retail property operated by Kesko. The system became operational in November 2025, providing a tangible example of data center thermal energy being put to productive use. In Helsinki, AI infrastructure developer OnZero has reached an agreement with municipal energy company Helen to connect a planned AI data center to the district heating network. The project is designed to supply as much as 525,000 MWh of recovered heat annually once operational, with service targeted for 2027. Denmark offers another variation on the approach. atNorth and Selected Group are advancing a project in Ølgod that would use surplus heat from atNorth’s DEN02 campus to support greenhouse food production. In October, Selected Group reported approval of the local development plan. The first phase involves two 10-hectare greenhouses, with the initial

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AI datacenter capex will far exceed forecast revenue through 2030

Investors are pouring more into AI data center infrastructure than they can hope to recoup by 2030, according to Gartner. “Enterprise and consumer AI revenue will be insufficient to support profitable returns on all planned AI capital expenditures during this period,” said Gartner Senior Director Analyst Will Sommer. AI data center owners would need to generate $8.5 trillion in revenue through 2030 to cover announced capital spending, financing costs and expected investment returns, he said. “However, total enterprise and consumer AI revenue that can be attributed to AI data centers during this period is forecast to reach no more than $3.6 trillion.”

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OCP 2026: AI Infrastructure Moves From Scaling to Coordination

Google: Using AI to Engineer the AI Factory Google Vice President and Engineering Fellow Parthasarathy Ranganathan closed the keynote program by extending the discussion beyond infrastructure designed for AI toward infrastructure increasingly designed and operated with AI. The distinction was central to his presentation. For several years, hyperscalers have pursued progressively more sophisticated custom systems to meet the demands of artificial intelligence. Google’s own approach has included TPUs, custom CPUs, optical networking, power distribution, liquid cooling, and extensive hardware-software co-design. But Ranganathan argued that the next opportunity involves applying AI directly to the processes of systems engineering. Google has already been pursuing the concept of agile and fungible data centers, capable of accommodating changing generations of computing hardware without requiring fundamental redesigns of every supporting system. Its OCP contributions include Project Deschutes, an open cooling distribution unit architecture; Project Brazos, intended to bring liquid cooling capabilities into existing air-cooled facilities; and power distribution specifications supporting higher-voltage DC architectures. Ranganathan also highlighted collaborative work involving Google, Meta, Microsoft, and NVIDIA on specifications intended to improve coordination between AI data centers and utility systems. Those efforts address the physical infrastructure required to support AI. The next stage, he suggested, is to apply AI to the engineering processes themselves. Google is exploring AI-assisted hardware architecture, automated software vulnerability detection and repair, data center climate modeling, and infrastructure capacity planning. Among the examples Ranganathan discussed were agentic systems capable of evaluating computer architecture choices and accelerating engineering workflows, alongside AI-assisted tools for software maintenance and operational optimization. In data center design, the company is applying AI and climate modeling to decisions involving cooling capacity, extreme weather risk, and long-term infrastructure investment. The approach could enable more site-specific designs, reducing the need to rely on broad assumptions about operating conditions throughout a facility’s useful life.

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OPC Global Summit: News spans AI-optimized hardware, cooling equipment

Arm announces methodology for evaluating CPU performance Arm released a white paper, developed with partners, that proposes a use-case-driven methodology for measuring the CPU’s contribution to agentic AI systems. It defines five representative workload types—software engineering, enterprise operations, financial and scientific research, personal assistants, and robotics—and pairs component-level benchmarking with end-to-end task measurement built on re-playable agent trajectories. Arm also highlighted independent testing by Signal65 that found its AGI CPU could deliver up to twice the rack-level performance of AMD’s Epyc Turin generation server processors across select agentic AI workloads. The comparison emphasizes completed work under rack-level constraints rather than conventional CPU benchmarks alone. In the testing, an Arm AGI CPU averaged 1.7x better performance for tooling, 2.0x for agentic workflows and orchestration, and 2.2x for reasoning and planning in modeled per-rack performance versus an AMD Epyc Turin processor. Arm’s AGI CPU reduces sandbox creation latency by up to 45% versus AMD Epyc 9965, helping agents spin up isolated execution environments faster, according to Arm. Kioxia introduces SSDs for AI Kioxia unveiled its LD4 Series enterprise solid-state drives (SSD), designed to increase flash storage density in hyperscale data centers and high density 1U servers. The drives target read-intensive workloads, including data storage applications supporting artificial intelligence infrastructure.

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

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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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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Here’s the tea

When Tristan Brown, an associate professor of history at MIT, first examined a Chinese tea chest label displayed at Old South Meeting House, he had

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