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How Capital One built production multi-agent AI workflows to power enterprise use cases

How do you balance risk management and safety with innovation in agentic systems — and how do you grapple with core considerations around data and model selection? In this VB Transform session, Milind Naphade, SVP, technology, of AI Foundations at Capital One, offered best practices and lessons learned from real-world experiments and applications for deploying and scaling an agentic workflow. [embedded content] Capital One, committed to staying at the forefront of emerging technologies, recently launched a production-grade, state-of-the-art multi-agent AI system to enhance the car-buying experience. In this system, multiple AI agents work together to not only provide information to the car buyer, but to take specific actions based on the customer’s preferences and needs. For example, one agent communicates with the customer. Another creates an action plan based on business rules and the tools it is allowed to use. A third agent evaluates the accuracy of the first two, and a fourth agent explains and validates the action plan with the user. With over 100 million customers using a wide range of other potential Capital One use case applications, the agentic system is built for scale and complexity. “When we think of improving the customer experience, delighting the customer, we think of, what are the ways in which that can happen?” Naphade said. “Whether you’re opening an account or you want to know your balance or you’re trying to make a reservation to test a vehicle, there are a bunch of things that customers want to do. At the heart of this, very simply, how do you understand what the customer wants? How do you understand the fulfillment mechanisms at your disposal? How do you bring all the rigors of a regulated entity like Capital One, all the policies, all the business rules, all the constraints, regulatory and otherwise?” Agentic AI was clearly the next step, he said, for internal as well as customer-facing use cases. Designing an agentic workflow Financial institutions have particularly stringent requirements when designing any workflow that supports customer journeys. And Capital One’s applications include a number of complex processes as customers raise issues and queries leveraging conversational tools. These two factors made the design process especially complex, requiring a holistic view of the entire journey — including how both customers and human agents respond, react, and reason at every step. “When we looked at how humans do reasoning, we were struck by a few salient facts,” Naphade said. “We saw that if we designed it using multiple logical agents, we would be able to mimic human reasoning quite well. But then you ask yourself, what exactly do the different agents do? Why do you have four? Why not three? Why not 20?” They studied customer experiences in the historic data: where those conversations go right, where they go wrong, how long they should take and other salient facts. They learned that it often takes multiple turns of conversation with an agent to understand what the customer wants, and any agentic workflow needs to plan for that, but also be completely grounded in an organization’s systems, available tools, APIs, and organizational policy guardrails. “The main breakthrough for us was realizing that this had to be dynamic and iterative,” Naphade said. “If you look at how a lot of people are using LLMs, they’re slapping the LLMs as a front end to the same mechanism that used to exist. They’re just using LLMs for classification of intent. But we realized from the beginning that that was not scalable.” Taking cues from existing workflows Based on their intuition of how human agents reason while responding to customers, researchers at Capital One developed  a framework in which  a team of expert AI agents, each with different expertise, come together and solve a problem. Additionally, Capital One incorporated robust risk frameworks into the development of the agentic system. As a regulated institution, Naphade noted that in addition to its range of internal risk mitigation protocols and frameworks,”Within Capital One, to manage risk, other entities that are independent observe you, evaluate you, question you, audit you,” Naphade said. “We thought that was a good idea for us, to have an AI agent whose entire job was to evaluate what the first two agents do based on Capital One policies and rules.” The evaluator determines whether the earlier agents were successful, and if not, rejects the plan and requests the planning agent to correct its results based on its judgement of where the problem was. This happens in an iterative process until the appropriate plan is reached. It’s also proven to be a huge boon to the company’s agentic AI approach. “The evaluator agent is … where we bring a world model. That’s where we simulate what happens if a series of actions were to be actually executed. That kind of rigor, which we need because we are a regulated enterprise – I think that’s actually putting us on a great sustainable and robust trajectory. I expect a lot of enterprises will eventually go to that point.” The technical challenges of agentic AI Agentic systems need to work with fulfillment systems across the organization, all with a variety of permissions. Invoking tools and APIs within a variety of contexts while maintaining high accuracy was also challenging — from disambiguating user intent to generating and executing a reliable plan. “We have multiple iterations of experimentation, testing, evaluation, human-in-the-loop, all the right guardrails that need to happen before we can actually come into the market with something like this,” Naphade said. “But one of the biggest challenges was we didn’t have any precedent. We couldn’t go and say, oh, somebody else did it this way. How did that work out? There was that element of novelty. We were doing it for the first time.” Model selection and partnering with NVIDIA In terms of models, Capital One is keenly tracking academic and industry research, presenting at conferences and staying abreast of what’s state of the art. In the present use case, they used open-weights models, rather than closed, because that allowed them significant customization. That’s critical to them, Naphade asserts, because competitive advantage in AI strategy relies on proprietary data. In the technology stack itself, they use a combination of tools, including in-house technology, open-source tool chains, and NVIDIA inference stack. Working closely with NVIDIA has helped Capital One get the performance they need, and collaborate on industry-specific  opportunities in NVIDIA’s library, and prioritize features for the Triton server and their TensoRT LLM. Agentic AI: Looking ahead Capital One continues to deploy, scale, and refine AI agents across their business. Their first multi-agentic workflow was Chat Concierge, deployed through the company’s auto business. It was designed to support both auto dealers and customers with the car-buying process.  And with rich customer data, dealers are identifying serious leads, which has improved their customer engagement metrics significantly — up to 55% in some cases. “They’re able to generate much better serious leads through this natural, easier, 24/7 agent working for them,” Naphade said. “We’d like to bring this capability to [more] of our customer-facing engagements. But we want to do it in a well-managed way. It’s a journey.”

How do you balance risk management and safety with innovation in agentic systems — and how do you grapple with core considerations around data and model selection? In this VB Transform session, Milind Naphade, SVP, technology, of AI Foundations at Capital One, offered best practices and lessons learned from real-world experiments and applications for deploying and scaling an agentic workflow.

Capital One, committed to staying at the forefront of emerging technologies, recently launched a production-grade, state-of-the-art multi-agent AI system to enhance the car-buying experience. In this system, multiple AI agents work together to not only provide information to the car buyer, but to take specific actions based on the customer’s preferences and needs. For example, one agent communicates with the customer. Another creates an action plan based on business rules and the tools it is allowed to use. A third agent evaluates the accuracy of the first two, and a fourth agent explains and validates the action plan with the user. With over 100 million customers using a wide range of other potential Capital One use case applications, the agentic system is built for scale and complexity.

“When we think of improving the customer experience, delighting the customer, we think of, what are the ways in which that can happen?” Naphade said. “Whether you’re opening an account or you want to know your balance or you’re trying to make a reservation to test a vehicle, there are a bunch of things that customers want to do. At the heart of this, very simply, how do you understand what the customer wants? How do you understand the fulfillment mechanisms at your disposal? How do you bring all the rigors of a regulated entity like Capital One, all the policies, all the business rules, all the constraints, regulatory and otherwise?”

Agentic AI was clearly the next step, he said, for internal as well as customer-facing use cases.

Designing an agentic workflow

Financial institutions have particularly stringent requirements when designing any workflow that supports customer journeys. And Capital One’s applications include a number of complex processes as customers raise issues and queries leveraging conversational tools. These two factors made the design process especially complex, requiring a holistic view of the entire journey — including how both customers and human agents respond, react, and reason at every step.

“When we looked at how humans do reasoning, we were struck by a few salient facts,” Naphade said. “We saw that if we designed it using multiple logical agents, we would be able to mimic human reasoning quite well. But then you ask yourself, what exactly do the different agents do? Why do you have four? Why not three? Why not 20?”

They studied customer experiences in the historic data: where those conversations go right, where they go wrong, how long they should take and other salient facts. They learned that it often takes multiple turns of conversation with an agent to understand what the customer wants, and any agentic workflow needs to plan for that, but also be completely grounded in an organization’s systems, available tools, APIs, and organizational policy guardrails.

“The main breakthrough for us was realizing that this had to be dynamic and iterative,” Naphade said. “If you look at how a lot of people are using LLMs, they’re slapping the LLMs as a front end to the same mechanism that used to exist. They’re just using LLMs for classification of intent. But we realized from the beginning that that was not scalable.”

Taking cues from existing workflows

Based on their intuition of how human agents reason while responding to customers, researchers at Capital One developed  a framework in which  a team of expert AI agents, each with different expertise, come together and solve a problem.

Additionally, Capital One incorporated robust risk frameworks into the development of the agentic system. As a regulated institution, Naphade noted that in addition to its range of internal risk mitigation protocols and frameworks,”Within Capital One, to manage risk, other entities that are independent observe you, evaluate you, question you, audit you,” Naphade said. “We thought that was a good idea for us, to have an AI agent whose entire job was to evaluate what the first two agents do based on Capital One policies and rules.”

The evaluator determines whether the earlier agents were successful, and if not, rejects the plan and requests the planning agent to correct its results based on its judgement of where the problem was. This happens in an iterative process until the appropriate plan is reached. It’s also proven to be a huge boon to the company’s agentic AI approach.

“The evaluator agent is … where we bring a world model. That’s where we simulate what happens if a series of actions were to be actually executed. That kind of rigor, which we need because we are a regulated enterprise – I think that’s actually putting us on a great sustainable and robust trajectory. I expect a lot of enterprises will eventually go to that point.”

The technical challenges of agentic AI

Agentic systems need to work with fulfillment systems across the organization, all with a variety of permissions. Invoking tools and APIs within a variety of contexts while maintaining high accuracy was also challenging — from disambiguating user intent to generating and executing a reliable plan.

“We have multiple iterations of experimentation, testing, evaluation, human-in-the-loop, all the right guardrails that need to happen before we can actually come into the market with something like this,” Naphade said. “But one of the biggest challenges was we didn’t have any precedent. We couldn’t go and say, oh, somebody else did it this way. How did that work out? There was that element of novelty. We were doing it for the first time.”

Model selection and partnering with NVIDIA

In terms of models, Capital One is keenly tracking academic and industry research, presenting at conferences and staying abreast of what’s state of the art. In the present use case, they used open-weights models, rather than closed, because that allowed them significant customization. That’s critical to them, Naphade asserts, because competitive advantage in AI strategy relies on proprietary data.

In the technology stack itself, they use a combination of tools, including in-house technology, open-source tool chains, and NVIDIA inference stack. Working closely with NVIDIA has helped Capital One get the performance they need, and collaborate on industry-specific  opportunities in NVIDIA’s library, and prioritize features for the Triton server and their TensoRT LLM.

Agentic AI: Looking ahead

Capital One continues to deploy, scale, and refine AI agents across their business. Their first multi-agentic workflow was Chat Concierge, deployed through the company’s auto business. It was designed to support both auto dealers and customers with the car-buying process.  And with rich customer data, dealers are identifying serious leads, which has improved their customer engagement metrics significantly — up to 55% in some cases.

“They’re able to generate much better serious leads through this natural, easier, 24/7 agent working for them,” Naphade said. “We’d like to bring this capability to [more] of our customer-facing engagements. But we want to do it in a well-managed way. It’s a journey.”

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Energy Department Expands Commitment to Collaboration with Norway on Water Power Research and Development

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Department of Energy Releases Report on Evaluating U.S. Grid Reliability and Security

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However, even though colocation and on-premises data centers will continue to lose share, they will still continue to grow. They just won’t be growing as fast as hyperscalers. So, it creates the illusion of shrinkage when it’s actually just slower growth. In fact, after a sustained period of essentially no growth, on-premises data center capacity is receiving a boost thanks to genAI applications and GPU infrastructure. “While most enterprise workloads are gravitating towards cloud providers or to off-premise colo facilities, a substantial subset are staying on-premise, driving a substantial increase in enterprise GPU servers,” said John Dinsdale, a chief analyst at Synergy Research Group.

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He pointed out that, in addition to its continued growth, OCI has a remaining performance obligation (RPO) — total future revenue expected from contracts not yet reported as revenue — of $138 billion, a 41% increase, year over year. The company is benefiting from the immense demand for cloud computing largely driven by AI models. While traditionally an enterprise resource planning (ERP) company, Oracle launched OCI in 2016 and has been strategically investing in AI and data center infrastructure that can support gigawatts of capacity. Notably, it is a partner in the $500 billion SoftBank-backed Stargate project, along with OpenAI, Arm, Microsoft, and Nvidia, that will build out data center infrastructure in the US. Along with that, the company is reportedly spending about $40 billion on Nvidia chips for a massive new data center in Abilene, Texas, that will serve as Stargate’s first location in the country. Further, the company has signaled its plans to significantly increase its investment in Abu Dhabi to grow out its cloud and AI offerings in the UAE; has partnered with IBM to advance agentic AI; has launched more than 50 genAI use cases with Cohere; and is a key provider for ByteDance, which has said it plans to invest $20 billion in global cloud infrastructure this year, notably in Johor, Malaysia. Ellison’s plan: dominate the cloud world CTO and co-founder Larry Ellison announced in a recent earnings call Oracle’s intent to become No. 1 in cloud databases, cloud applications, and the construction and operation of cloud data centers. He said Oracle is uniquely positioned because it has so much enterprise data stored in its databases. He also highlighted the company’s flexible multi-cloud strategy and said that the latest version of its database, Oracle 23ai, is specifically tailored to the needs of AI workloads. Oracle

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CISPE’s response to the European Commission’s report warns that the resulting regulatory uncertainty could hurt the region’s economy. “Imposing new, standalone water regulations could increase costs, create regulatory fragmentation, and deter investment. This risks shifting infrastructure outside the EU, undermining both sustainability and sovereignty goals,” CISPE said in its latest policy recommendation, Advancing water resilience through digital innovation and responsible stewardship. “Such regulatory uncertainty could also reduce Europe’s attractiveness for climate-neutral infrastructure investment at a time when other regions offer clear and stable frameworks for green data growth,” it added. CISPE’s recommendations are a mix of regulatory harmonization, increased investment, and technological improvement. Currently, water reuse regulation is directed towards agriculture. Updated regulation across the bloc would encourage more efficient use of water in industrial settings such as datacenters, the asosciation said. At the same time, countries struggling with limited public sector budgets are not investing enough in water infrastructure. This could only be addressed by tapping new investment by encouraging formal public-private partnerships (PPPs), it suggested: “Such a framework would enable the development of sustainable financing models that harness private sector innovation and capital, while ensuring robust public oversight and accountability.” Nevertheless, better water management would also require real-time data gathered through networks of IoT sensors coupled to AI analytics and prediction systems. To that end, cloud datacenters were less a drain on water resources than part of the answer: “A cloud-based approach would allow water utilities and industrial users to centralize data collection, automate operational processes, and leverage machine learning algorithms for improved decision-making,” argued CISPE.

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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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