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The rise of browser-use agents: Why Convergence’s Proxy is beating OpenAI’s Operator

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new wave of AI-powered browser-use agents is emerging, promising to transform how enterprises interact with the web. These agents can autonomously navigate websites, retrieve information, and even complete transactions – but early testing reveals significant […]

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A new wave of AI-powered browser-use agents is emerging, promising to transform how enterprises interact with the web. These agents can autonomously navigate websites, retrieve information, and even complete transactions – but early testing reveals significant gaps between promise and performance.

While consumer examples offered by OpenAI’s new browser-use agent Operator, like ordering pizza or buying game tickets, have grabbed headlines, the question is about where the main developer and enterprise use cases are. “The thing that we don’t know is what will be the killer app,” said Sam Witteveen, co-founder of Red Dragon, a company that develops AI agent applications. “My guess is it’s going to be things that just take time on the web that you don’t actually enjoy.” This includes things like going on the web and searching for the cheapest price of a product or booking the best hotel accommodations. More likely it will be used in combination with other tools like Deep Research, where companies can then do even more sophisticated research plus execution of tasks around the web.

Companies need to carefully evaluate the rapidly evolving landscape as established players and startups take different approaches to solving the autonomous browsing challenge.

Key players in the browser-use agent landscape

The field has quickly become crowded with both major tech companies and innovative startups:

Operator and Proxy are the most advanced, in terms of being consumer-friendly and out-of-the-box ready. Many of the others appear to be positioning themselves more for developer or enterprise usage. For example, Browser Use, a Y-Combinator startup that allows users to customize the models used with the agent. This gives you more control over how the agent works, including using a model from your local machine. But it’s definitely more involved.

The others listed above provide a varying degree of functionality and interaction with local machine resources. I decided not even to test ByteDance’s UI-TARS for now, because it requested lower level access to my machine’s security and privacy features (if I test it out, I’ll definitely use a secondary computer). 

Testing reveals reasoning challenges

So the easiest to test are OpenAI’s Operator and Convergence’s Proxy. In our testing, the results highlighted how reasoning capabilities can matter more than raw automation features. Operator, in particular, was more buggy.

For example, I asked the agents to find and summarize VentureBeat’s five most popular stories. It was an ambiguous task, because VentureBeat doesn’t have a “most popular” section per se. Operator struggled with this. It first fell into an infinite scrolling loop while searching for ‘most popular’ stories, requiring manual intervention. In another attempt, it found a three-year-old article titled “Top five stories of the week.” In contrast, Proxy demonstrated better reasoning by identifying the five most visible stories on the homepage as a practical proxy for popularity, and it gave accurate summaries.

The distinction became even clearer in real-world tasks. I asked the agents to book a reservation at a romantic restaurant for noon in Napa, California. Operator approached the task linearly — finding a romantic restaurant first, then checking availability at noon. When no tables were available, it reached a dead end. Proxy showed more sophisticated reasoning by starting with OpenTable to find restaurants that were both romantic and available at the desired time. It even came back with a slightly better rated restaurant.

Even seemingly simple tasks revealed important differences. When searching for a “YubiKey 5C NFC price” on Amazon, Proxy quickly found the item more easily than Operator. 

OpenAI hasn’t divulged much about technologies it uses for training its Operator agent, other than saying it has trained its model on browser-use tasks. Convergence, however, has provided more detail: Its agent uses something called Generative Tree Search to “leverage Web-World Models that predict the state of the web after a proposed action has been taken. These are generated recursively to produce a tree of possible futures that are searched over to select the next optimal action, as ranked by our value models. Our Web-World models can also be used to train agents in hypothetical situations without generating a lot of expensive data.” (More here).

Benchmarks may be useless for now

On paper, these tools appear closely matched. Convergence’s Proxy achieves 88% on the WebVoyager benchmark, which evaluates web agents across 643 real-world tasks on 15 popular websites like Amazon and Booking.com. OpenAI’s Operator scores 87%, while Browser-Use says it reaches 89% but only after changing the WebVoyager codebase slightly, it conceded, “according to our needs”.

These benchmark scores should really be taken with a grain of salt, though, as they can be gamed. The real test comes in practical usage for real-world cases. It’s very early, the space is so rapidly changing, and these products are changing almost on a daily basis. The results will depend more on the specific jobs you’re trying to do, and you may want to instead rely on the vibes you get while using the different products.

Enterprise implications

The implications for enterprise automation are significant. As Witteveen points out in our video podcast conversation about this, where we do a deep dive into this browser-use trend, many companies are currently paying for virtual assistants – operated by real people – to handle basic web research and data gathering tasks. These browser-use agents could dramatically change that equation.

“If AI takes this over,” Witteveen notes, “that’s going to be some of the first low hanging fruit of people losing their jobs. It’s going to show up in some of these kinds of things.”

This could feed into the robotic process automation (RPA) trend, where browser use is pulled in as just another tool for companies to automate more tasks. And as mentioned earlier, the more powerful uses cases will be when an agent combined browser use with other tools, including things like Deep Research, where an LLM-driven agent uses a search tool plus browser use to do more sophisticated jobs.

Cost dynamics driving innovation

Another key factor driving rapid development is the availability of powerful open-source reasoning models like DeepSeek-R1. This allows companies building these browser-use agents to compete effectively with larger players by leveraging these models rather than building their own.

The pricing pressure is already evident. While OpenAI requires a $200 monthly ChatGPT Pro subscription to access Operator, Convergence offers limited free use (up to five uses per day) and a $20/month unlimited plan. This competitive dynamic should accelerate enterprise adoption, though clear use cases are still emerging.

Security and integration challenges

Several hurdles remain before widespread enterprise adoption. Some websites actively block automated browsing, while others require CAPTCHA verification. While OpenAI and Convergence have tools that can get past CAPTCHAs, they let users take over the task to fill them out — instead of doing them directly, since the whole point of CAPTCHAs is to ensure a human is at the other end. Tools like ByteDance’s UI-TARS request deep system access, which raises security concerns for enterprise deployment.

Additionally, the approach to website cooperation varies. OpenAI has worked with specific partners like Instacart, Priceline, DoorDash and Etsy, while others attempt to navigate any website. This inconsistency could impact reliability for enterprise use cases. And of course, any time an agent hits a site requiring login details, that will slow things — as the agents will turn things over to you to fill in those details.

Looking ahead

For enterprises evaluating these tools, the focus should be on specific use cases where autonomous web interaction could provide clear value – whether in research, customer service, or process automation. The technology is progressing rapidly, but success will depend on matching capabilities to concrete business needs.

As this space evolves, expect to see more enterprise-focused features and potentially specialized agents for specific industries or tasks. The race between established players and innovative startups should drive both technical advancement and competitive pricing, making 2025 a crucial year for enterprise browser-use agent adoption.

For more detail on these trends and testing results, check out the full video conversation between Sam Witteveen and myself.

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SonicWall reports two major security holes under active exploit

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Palo Alto Networks buys Console to boost agentic security

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Mainframe shops tap AI for system insights and recommendations

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San Matías Pipeline secures $900 million for Vaca Muerta-to-LNG gas pipeline

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Petrobras readies for two more FPSOs to sail away to Búzios field

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Matador CFO: Hormuz resolution won’t change ‘grower’ mindset

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US, Venezuela reach deal covering 65 billion bbl of oil reserves

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Oil prices fall on Strait of Hormuz bypass tactics

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Insights: State actors, ransomware, and the offshore security gap

  Offshore energy infrastructure faces an increasingly complex mix of cyber and physical security threats as digitalization, remote operations, and geopolitical tensions reshape the risk landscape. In this Insights episode of the Oil & Gas Journal ReEnterprised podcast, OGJ Upstream Editor Alex Procyk discusses how offshore installations are becoming targets for ransomware attacks, navigation-system interference, supply-chain compromise, and physical sabotage. There is a growing shift from opportunistic cybercrime toward state-linked and organized criminal actors seeking long-term access to critical infrastructure. As operational technology (OT) and information technology (IT) systems become more interconnected, attackers are increasingly exploiting remote access pathways to move from corporate networks into critical operational systems. Traditional perimeter-based security models are proving inadequate for modern offshore operations and outlines emerging approaches such as cyber-informed engineering, OT-focused monitoring, micro-segmentation, defense-in-depth strategies, and integrated cyber-physical security frameworks designed to improve resilience across offshore platforms, vessels, ports, and subsea infrastructure. Cyber-informed engineering and OT-native security controls are emerging as key defensive strategies.

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Dell’s $95B AI backlog shows the infrastructure crunch is far from over

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VMware by Broadcom: Product, service and support news

Customer concerns loom as VMware Explore event approaches Aug. 19, 2024: This year’s VMware Explore marks the first time for the flagship customer event since Broadcom finalized its acquisition of VMware last November. Enterprise customers have questions about VMware’s future direction, licensing changes, and product roadmap following Broadcom’s takeover. Customers want to continue to see innovation across the VMware portfolio, says one analyst. They also want to see Broadcom focus on integration, interface design, and an easier adoption path.  Broadcom distrust drives sales for VMware competition Aug. 14, 2024: Concerns about the new direction VMware is taking are driving some enterprise customers to consider alternative platforms from vendors such as Scale Computing, Nutanix and Oxide Computer. Scale Computing said in its most recent quarterly earnings announcement that sales have taken off and its new customers have doubled over the past year, thanks in part to Broadcom’s changes to VMware sales operations. Nutanix hunts disgruntled VMware customers July 01, 2024: Nutanix began aggressively courting VMware customers who might be open to jumping ship in the wake of VMware’s purchase by Broadcom and some of the unpopular moves that followed. In addition, Nutanix recently signed key partnerships with some unlikely on-and-off competitors: Dell, Cisco and HPE.  Broadcom tosses VMware users a bone, extends vSphere 7 support six months July 25, 2024: Broadcom has announced that VMware vSphere 7.x users will get six additional months of support for the product. VMware vSphere 7, which was launched in 2020, was scheduled to go out of support in April 2025 but will now be maintained until October 2025. Broadcom bolsters VMware Edge Compute Stack June 26, 2024: A slew of updates in VMware ECS 3.5 are aimed at helping customers more easily manage edge devices, applications, and infrastructure across multiple locations. Updates include zero-touch orchestration capabilities, pull-based architecture, and edge

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AMD Helios Takes AI Infrastructure Fight to Rack Scale

AMD is escalating its challenge to Nvidia with Helios, a rack-scale AI system that puts the company squarely into the race to define how the next generation of AI factories are built. Unveiled in production form at AMD’s Advancing AI 2026 event in San Francisco, Helios combines 72 Instinct MI455X GPUs with sixth-generation EPYC “Venice” CPUs, Pensando networking and AMD’s ROCm software stack. The significance goes beyond another generation of faster accelerators. Like Nvidia’s Vera Rubin platform, Helios treats the rack as an integrated compute system in which GPUs, CPUs, memory, networking, power delivery and cooling increasingly have to be engineered together. For data center operators, that means the competitive battle between the two chip companies is moving directly into infrastructure design. AMD said Helios is now in production, with deployments beginning during the second half of 2026. The Rack Becomes the System Helios is built around AMD’s Instinct MI455X, a liquid-cooled accelerator based on the company’s CDNA 5 architecture and equipped with HBM4 memory. A complete Helios rack delivers 72 GPUs along with EPYC host CPUs and Pensando networking for front-end, scale-up and scale-out traffic. AMD is positioning the platform for both large-scale training and increasingly important inference workloads. AMD says Helios can deliver up to 30% more inference tokens per dollar than a competing system. The company also claims the MI455X provides more peak AI compute and substantially greater memory capacity than Nvidia’s Rubin GPU. Those numbers are AMD benchmarks rather than independent comparisons. But the larger architecture may matter more than the percentages. AI infrastructure is rapidly moving beyond the model of servers being installed as largely independent pieces of IT equipment. Accelerators have to exchange enormous volumes of data with each other while CPUs orchestrate workloads and networking connects increasingly large clusters across rows, halls and

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Moses Lake Moves From Bitcoin to AI and HPC

Moses Lake and the Quincy Effect Moses Lake should not be understood as an isolated rural data center project. It sits within the larger Grant County infrastructure ecosystem that helped make nearby Quincy one of the defining hyperscale markets of the cloud era. Keel has called Moses Lake “adjacent to one of the most proven data center markets in the United States,” noting that hyperscale infrastructure has operated around Quincy for nearly two decades. In its Q1 remarks, management argued that increasingly constrained regional power leaves operators seeking incremental Pacific Northwest capacity with fewer options. The Grant County Economic Development Council’s data center inventory includes Microsoft, NTT Data, Sabey, Vantage, Intuit and other operators. The organization counts more than 1.5 million square feet of data center operations in the county and points to a diverse fiber network and Grant County PUD’s Columbia River hydroelectric resources as core advantages. That existing cluster changes the equation for an 18-MW project. The headline AI developments of 2026 are increasingly measured in hundreds of megawatts or gigawatts. But another market exists underneath those megacampus announcements: operators that need tens of megawatts in the right geography on a timeline measured in quarters rather than many years. An 18-MW facility with power, fiber, equipment and construction underway can therefore be strategically more relevant than its relatively modest capacity suggests. Keel had previously secured an option for another 10 MW near Moses Lake, but management said during its second-quarter call that it has relinquished that option and is now focused exclusively on the existing 18 MW. The decision further distinguishes Moses Lake from the industry’s race to advertise ever-larger pipelines. This project is about getting capacity online. A Second Life for Crypto Power That may ultimately be the larger Moses Lake story. Bitcoin miners assembled portfolios around

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ISE Expo 2026: DCF Takes Stage with JLL, TIA

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The Phantom Data Center Effect: When Perception Precedes Project Reality

Moving Beyond Speculation The answer is not simply earlier marketing campaigns or more aggressive public relations programs. Effective engagement requires understanding local concerns, motivations and political dynamics—and recognizing when community opposition reflects a durable constraint rather than a communications problem. We need to realize when no means no, and not interpret it as “try harder.” Phantom perception also can’t be handled by any one operator in any one market; this must be a collective, such as a crowd-sourced data platform, market by market. What our industry needs are clearer frameworks for evaluating digital infrastructure against these additional community-readiness criteria, because speculation is increasingly filling information gaps before formal projects reach the public process. Organizations such as OIX have begun working toward that objective. Its Digital Infrastructure Framework is modeled on traditional master planning and is intended to help communities evaluate what infrastructure they have, what they need and what they want as they plan for future technology requirements. The framework includes assessment criteria spanning investment readiness, policy, risk, sustainability and resilience. Greater transparency can narrow the gap between perception and reality. But greater transparency will not eliminate speculation, and unfortunately, it also won’t eliminate fear. Large infrastructure projects have always attracted public interest and scrutiny, and data centers are unlikely to become invisible again as AI demand accelerates. The question is how the industry responds to that visibility. The Next Stage of Data Center Development Community reaction to perceived data center development represents another potential source of site-selection intelligence. If communities begin reacting to a project before a developer has formally advanced one, that response can offer an early indication of whether a market is receptive to large-scale digital infrastructure or already approaching its political limit. This gives operators and investors another axis to measure: not just megawatts, fiber routes,

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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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