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.
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