Cognizant‘s Neuro AI platform, unveiled last year, is set to receive an AI boost as the consultancy firm introduces multi-agent functionalities to the service.
The Neuro AI platform enables organisations to conceptualise, prototype and evaluate generative AI applications without coding. Babak Hodjat, Cognizant’s CTO of AI, informed VentureBeat that the service was previously managed by Cognizant’s experts for clients. However, Neuro AI will now be accessible for enterprises to utilise independently.
“As we began demonstrating it to clients, one of the things we noticed was them saying, ‘This is truly fascinating, we want to use it ourselves and host it in-house,'” Hodjat explained. “In a way, they started viewing it as a factory that generates ideas for applying generative AI in their businesses.”
Hodjat highlighted that Neuro AI’s utilization of multiple agents distinguishes it from other AI application platforms—a feature Cognizant was already examining as it adapted the service for clients.AI agents have, of course, become a significant trend in enterprise AI this year.
The platform comprises four stages, all relying on pre-configured agents: the Opportunity Finder, Scoping Agent, Data Generator, and Model Orchestrator.
It functions as a Cognizant consultant for clients aiming to build applications. The platform guides users through the process of ideating an application and ultimately provides a framework for the customer to follow.
When users first engage with Neuro AI, they’re prompted to describe the issues they wish to address. The Opportunity Finder then deploys agents to search for industry-specific use cases. Once a potential use case is identified, users progress to the Scoping agent, which illustrates the use case’s impact on specific categories and performance indicators. The Data Generation agent creates synthetic data related to the use case to test the application.
The Model Orchestrator configures the application. Hodjat explained that it employs several agents that make calls to construct the system. For instance, a project describer agent will return a JSON description followed by a context agent or an outcome mapper. The number of agents the Orchestrator manages depends on the specific use case.
“We had the agents communicate with each other to identify necessary capabilities,” Hodjat said.”We accomplished this by encapsulating each agent’s specific expertise, enabling interaction between them. One agent asks another, ‘I have this use case to build. Can you assist me?’ The key here is facilitating communication between the agents.”
Hodjat explained that his team utilised LangChain as a framework to develop their multi-agent orchestration whilst remaining LLM-agnostic. He noted that whilst the framework isn’t flawless, it was crucial for Neuro AI to support both open and closed models, as many clients prefer using different models.
AI application consulting competition intensifies
This isn’t Cognizant’s first venture into generative AI. In March, they inaugurated an AI lab in San Francisco to promote enterprise adoption of the technology.
Firms like Cognizant, which assist other enterprises in establishing their own AI applications or programmes, are crafting new offerings to simplify generative AI use. Accenture, in partnership with AWS, introduced a platform that assesses AI readiness and responsible AI policies. McKinsey and Company developed a chatbot for its consultants called Lilli last year.
Consulting and business process service providers are carving out their niche in the increasingly competitive AI platform arena. Enterprise software providers, such as Salesforce, SAP and Oracle, already offer customers access to platforms for easy creation of agents or other AI applications. Organisations like Cognizant are developing products that appear to cater to businesses still uncertain about fully harnessing generative AI.
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