Cognizant Enhances Neuro AI Platform with Multi-Agent Functionality for Streamlined Enterprise AI Deployment

Cognizant adds multi-agent functionality to AI application platform

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Cognizant's Neuro AI platform, announced last year, will get more AI as the consultancy adds multi-agent capabilities to the service. The Neuro AI platform helps organizations ideate, prototype and test generative AI applications without coding. Babak Hodjat, Cognizant's CTO of AI, told VentureBeat the service used to be something Cognizant's experts did for customers. However, Neuro AI will now be available for enterprises to use themselves. "One of the things we rain into as we started demoing it to clients was them saying, hey, this is really fascinating, we want to use it ourselves and host it in-house," Hodjat said. "In some ways, they started thinking of it as this factory that generates ideas for where to apply generative AI in their businesses." Hodjat said Neuro AI's use of multiple agents makes it stand out from other AI app platforms, which Cognizant was already exploring while reconfiguring the service for clients. AI agents, of course, have become a big trend for enterprise AI this year. The platform has four steps, all of which rely on pre-configured agents: the Opportunity Finder, Scoping Agent, Data Generator and Model Orchestrator. It acts as a Cognizant consultant for clients who want to build applications. The platform goes through the process of ideating an application and, in the end, provides a framework for the customer to follow. When people first start using Neuro AI, they're asked to describe what issues they want solved. The Opportunity Finder then deploys agents to search for industry-specific use cases. Once a potential use case is identified, users then move to the Scoping agent, which will show the use case's impact on specific categories and performance indicators. The Data Generation agent will generate synthetic data related to the use case to test out the application. The Model Orchestrator sets up the application. Hodjat said it uses several agents that make calls to build out the system. For example, a project describer agent will return a JSON description followed by a context agent or an outcome mapper. The number of agents the Orchestrator will manage depends on the use case. "We had the agents communicate with each other to identify what capabilities are needed," Hodjat said. "We did that by encapsulating each agent's expertise so these agents are talking to each other. One agent is asking the other agent, hey, I have this use case to build. Can you do something for me? The main trick here is to actually have the agents in communicating with each other." Hodjat said his team used LangChain as a framework to build out its multi-agent orchestration and remain LLM agnostic. He said the framework is not perfect, but since many clients prefer to use different models, it was important Neuro AI can handle both open and closed models. Competition in AI application consulting is growing This is not Cognizant's first foray into generative AI. In March, it opened an AI lab in San Francisco to help boost enterprise use of the technology. Companies like Cognizant, which helps other enterprises set up their own AI applications or programs, are creating new product offerings to make using generative AI easier. Accenture, along with AWS, released a platform that evaluates AI readiness and responsible AI policies. McKinsey and Company set up a chatbot for its consultants called Lilli last year. Consulting and business process service providers are starting to create their niche in the increasingly competitive AI platform space. Enterprise software providers, like Salesforce, SAP and Oracle, already give customers access to platforms to easily create agents or other AI applications. Organizations like Cognizant are building products that seem to cater to businesses that are still unsure of how to harness generative AI fully.

Cognizant enhances Neuro AI platform for faster AI use case deployment - SiliconANGLE

Cognizant enhances Neuro AI platform for faster AI use case deployment Information technology services company Cognizant Technology Solutions Corp. today unveiled enhancements to its Neuro AI Platform that allow enterprises to discover, prototype and develop artificial intelligence use cases rapidly. The enhancements allow businesses to quickly identify and address key challenges by generating AI models using synthetic or anonymized data while providing predictive insights and decision-making guidance. The service also offers industry-specific configurations, allowing companies to scale AI use cases and drive measurable outcomes. The new additions seek to address the issue where, according to a Cognizant and Oxford Economics study, most enterprises are looking to leverage AI to create new revenue but struggle with implementing and scaling cross-enterprise use cases. The same study also found that 70% of enterprises don't think they're moving fast enough. The enhancements to Cognizant Neuro AI address these problems by allowing  business leaders to identify what business problems to tackle, scope them and generate synthetic data or import their own anonymized data to start creating AI models. The platform can then predict and provide guidance on meeting business outcomes while also justifying those decisions to allow businesses to assess the impact of a variety of use cases. Cognizant's upgraded Neuro AI platform introduces several advanced features, including a multi-agent discovery tool called Opportunity Finder and a suite of large language model assistants. Opportunity Finder helps businesses identify AI decisioning use cases through a guided approach to uncovering potential applications. Clients can then use the drag-and-drop Model Orchestrator tool to prepare data and apply machine learning models, streamlining the process with the help of LLMs. Once data preparation is complete, machine learning models are employed to predict outcomes, while AI models recommend decisions. The best-performing models can be further explored via a web interface or through interaction with LLM assistants to gain deeper insights and for fine-tuning. Cognizant says the multi-agent system enhances decision-making across a range of business challenges, making AI more accessible to enterprise leaders. The enhanced Cognizant Neuro AI platform comes with preconfigured templates designed for various industries. The configurations cater to industries such as healthcare, finance and agriculture to help businesses quickly implement use cases such as drug discovery, fraud prevention, crop yield optimization and supply chain management. "Businesses are struggling with how and where to apply AI to solve business problems and that's why we've seen most AI use cases limited to prediction-based outcomes or single LLM chat-based solutions," said Chief Technology Officer Babak Hodjat. "Multi-agent AI systems hold the key to solving these problems, which is why Neuro AI is now built with one at its core." "This platform puts business leaders - not just data scientists -- in the driver's seat, so they can tap into their own domain knowledge to quickly test and establish decision-making use cases for AI in minutes and then provide the resulting model code to iterate at scale," Hodjat added. Prashant Gaonkar, vice president of global strategy and planning of enterprise platforms at Cognizant, joined Dan McAllister, senior vice president of global alliances and channels at Boomi LP, on theCUBE, SiliconANGLE Media's livestreaming studio, in May, when he discussed the use of AI in integrating and automating data: