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How do you increase risk management and safety of risks and security in the agent systems and how do you fight the main considerations around the information and model? In that Turn a vb Mild Naphade, Christmas NAFTE, SVP, technology, real world experiences and agent, which are AI funds of AI funds in Capital One, offered applications and applications to place the workflow of real world experiences and agent.
Capital, which is committed to staying in the forefront of developing technologies, has recently launched a high-rise agent AI system to increase car purchase and sale experience. In this system, more than one AI agents work together to provide only a car recipient, but to provide special measures based on the customer’s choice and needs. For example, an agent communicates with the client. Another creates an action plan based on the means of operation, and the means permitted to use. The third agent assesses the accuracy of the first two and explains and confirms the action plan with the fourth agent user. With more than 100 million clients, another potential capital used a business application using a business applications that uses a business applications and is built for complexity.
“When you think of improving the customer’s experience, we are pleased with the customer and think what are the ways that can happen?” He said to Nosh. “If you want to open an account or try a vehicle, customers have a bunch of how they want. How do you understand what the customer wants?
Agentic AI, for attention, but also this step for the client-working situations was the next step open.
To design agent work flow
Financial institutions are especially tougher requirements when designing any work that supports customer trips. And the capital’s applications include a number of complex processes such as surveys that raise the problems of customers and using negotiations. The two factor design process is due to the reaction and reason for the reaction and cause of all steps, but also human agents, but also in human agents and the reaction and reason.
“When people look at how people think, we were struck by several brave facts.” “We saw that if we use a very logical agent, if we can imitate the thought of a person, what do you do exactly what do different agents do? Why are there four? Why? Why? Why? Why?
Historical data learned customer practices: These conversations need to be in the places where these conversations go right, and other clear facts. The client has made a large number of conversations with an agent to understand what the client wants and any agent needs to plan for this, but also the organization’s systems, existing means, API and API and organizational policy guards are completely justified.
“The main progress for us realized that this should be dynamic and iterative,” he said. “You look at many people using LLMS as the LLMs are as a front end to the same mechanism. Simply use the LLMS for the classification of the intention. But we realized before it is not different.”
Receives requests from existing workflows
When responding to customers, how the person’s agents were developed in Capital, each expert with a different experience with a different experience developed and developed a framework to solve the problem.
In addition, one of the capital includes strong risk frames for the development of the agent system. He noted that Naphade noted that Naphade said that the protocols and frameworks in the low-risk reduction protocols and frameworks, “other bodies, risks independent, please appreciate you, check you.” “We thought it was a good idea to us, and evaluated what the first two agents based on the capital’s policy and rules of the agent.”
The appraiser determines whether previous agents are successful, and if not, the plan rejects the plan and requires the planning agent to correct the results based on the verdict of the problem. This happens in an iTerminated process until the appropriate plan is achieved. It has also been proven to have a great grace for the company’s approach to the AI approach.
“Estimator Agent … is where we bring a world model.
Technical Problems of Agentic AI
Agentic systems should work with performance systems throughout the organization with all permissions. In various contexts while maintaining high accuracy, tools and APIs were difficult – it was difficult to prepare and execute a safe plan.
“Experience, test, evaluation, human-in-the-loop, all our right employees,” Naphade “,” Naphade “has no precedent. Oh, oh, someone else did it in this way. How was this been running? How was he used. We did this for the first time. “
Model selection and partner with NVIDIA
In terms of models, capital is interested in watching an academic and industrial research, presenting in conferences and aware of the situation of the art. In the present use, they used openly, not closed, openly, openly. This is very important for them, because the competitive advantage in Naphade AI strategy is reliant on property data.
The technology is using a combination of tools, including home technology, open source tool chains and NVIDIA Inference Stack. Work closely with NVIDIA, helped to take the performance of capital and cooperate in the properties of the industry and prioritize the features of the Triton server and their Tensort LLM.
Agentic AI: Looking forward
One Capital continues to place, scaling and cleans AI agents in their work. Their first multi-agent workflow was a Chat concierge placed in the company’s auto business. Both car dealers and customers are designed to support the process of purchase and sale of cars. Sales with rich customer data are seriously identifying leaders, which has significantly improved the size of customers and increased in some cases to 55%.
“It can create better serious leaders through natural, easier, easier, 24/7 agent,” he said. “We would like to bring this ability [more] of our signs encountered by the client. But we want to do it in a well-driven way. It’s a journey. “
