Cognizant Enhances Neuro AI Platform with Multi-Agent Capabilities for Improved Enterprise AI Adoption

Cognizant Gives Its Neuro AI Multi-Agent Capabilities For Better Decision-Making

'Neuro AI is our flagship AI platform,' Hodjat tells CRN. 'We use it to develop decision-making use cases for our clients. Now we've made it agent-based. It's a multi-agent-based system with humans in the loop to empower the platform and empower our user and our clients.' Cognizant said it has significantly enhanced its Cognizant Neuro AI platform as a way to help enterprises to discover, prototype, and develop AI use cases. The aim of the new version of Cognizant Neuro AI, unveiled this week, is to help businesses improve decision-making as a way to increase company performance and find new revenue opportunities, said Babak Hodjat, chief technology officer for AI for the Teaneck, N.J.-based Cognizant, ranked No. 8 on the CRN Solution Provider 500. The enhanced Cognizant Nero AI platform follows the July introduction of the Cognizant Neuro Edge, which brings AI and GenAI to enterprise businesses, especially manufacturers, working with edge AI from chips and devices to applications. Cognizant Neuro Edge is a generic framework aimed at accelerating the development of enterprise edge services. [Related: Cognizant Launches New Advanced AI Lab With Focus On Core AI Research] "Neuro AI is our flagship AI platform," Hodjat told CRN. "We use it to develop decision-making use cases for our clients. Now we've made it agent-based. It's a multi-agent-based system with humans in the loop to empower the platform and empower our user and our clients." For many people, their first gut reaction to AI and GenAI is that it will help find some patterns in their data and tell them what's going to happen in the future, and maybe provide some insights, Hodjat said. "That's a part of our platform," he said. "But what really differentiates it is that it then goes on to give you suggestions as to what to do. So those actions or decisions you want to make are not trivial. It's not very easy to make decisions, especially against more than one outcome at the same time, such as if you want to maximize revenue while minimizing risk while being sustainable. Those kinds of things don't always align. So the AI helps you find the best balance, the best decision strategy, by creating a digital twin, a machine-learning-based digital twin of the subject for decision-making." The new Cognizant Neuro AI platform is now agent-based so that its agent can collaborate with other agents to identify use cases to help simplify decision-making, Hodjat said. "So from that really first touch with the client, the AI agent is helping collaboratively identify the use cases that would give them the best, most impact, and then help them build the use cases, end to end," he said. Cognizant has already developed over 500 use cases on its Neuro platform for internal use and for work it has done with its clients, Hodjat said. "It's like an accelerator," he said. "We get in front of a client, and we use it. We help the client identify and build the use cases, and we take it from there. What's new is that the Neuro AI platform will now be available for clients to actually deploy and use it as sort of a use case generation factory to do GenAI agent-based use case generation. That's partly because many of our clients have demanded it. We've had clients saying, 'We want to bring this in, run it on our own data in-house, and have our own folks trained to use it.'" The Cognizant Neuro AI platform agents are all GenAI-based, but it also orchestrates non-GenAI-based AI techniques as well, he said. As an example, Hodjat said to consider a large retailer looking for ideas for new store locations. The user could ask the platform's scoping agent to guess what data would be available for a typical major US retailer when it comes to deciding where to open new stores. The platform might start looking for demographic, population density, household income, competition, and other data from public or internal sources. That data may be structured or unstructured, he said. "At that point, for a specific location to open a store, it could produce recommendations for what the store size, what market mix, what product mix, what kind of store format, investment level, market strategy for the launch, staffing levels, and so forth," he said. "The strategy is going to be informed by the outcomes. So it's going to want to obviously maximize our annual sales while maximizing our ROI and reducing our cost. The retailer has KPI (key performance indicators), and you want the AI to be aligned with those KPIs." The KPIs could be changed, or new data added, before moving on to the Neuro AI's data generator which can then autonomously work with other agents to write code to generate a use case, Hodjat said. Once the use case is double-checked by the user, the user can start training the model and the platform's predictor can predict the various outcomes, he said. Users can also add uncertainty to the model, he said. The Neuro AI platform can actually come up with predictions, as well as raise flags with it determines there is not enough data, Hodjat said "We can add things, like an LLM here for the front end, so I have a ChatGPT-like interface to it," he said. "I could add more LLMs. So if I had not generated the data or the data had missing values, I could have an LLM do that. I can actually go talk to the data." The platform lets the AI check the AI's work, Hodjat said. "That's something that we really do a lot," he said. "I can actually have the system review the design that it made and come back to me and say, 'OK, this design has these strengths, these weaknesses. Here's some things that I think you should do to improve it.'" Hodjat said he recently demonstrated the Cognizant Neuro AI Platform to a group of executives who were clamoring to do their own use cases. "In fact, one use case had to do with Hurricane Milton coming in and their logistics and so forth," he said. "And we built it on the fly. However, for us to actually build the use case end-to-end, we have to rely on synthetic data, and then that acts as a template. So then we take that in, and we do the hard work of mapping their real data and maybe third party or public data to that template, and can then make it work."

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: