Babak Hodjat, the co-founder of Sentient Technologies who helped build one of the world's largest distributed AI systems, now directs Cognizant's AI Research Labs.
Agentic AI, evolutionary computation, distributed intelligence: Babak Hodjat's research brief at Cognizant reads less like a job description and more like a map of where enterprise AI is trying to go.
Hodjat serves as Chief AI Officer at Cognizant, where he leads the company's AI Research Labs. His remit is unusually broad, spanning agentic AI, evolutionary computation and distributed intelligence, with a consistent emphasis on building AI systems engineered for large-scale enterprise deployment rather than laboratory demonstration. He arrives at the role as a longtime AI researcher, inventor and entrepreneur.
The entrepreneurial record underpins that brief. Hodjat co-founded Sentient Technologies, where he helped build one of the world's largest distributed AI systems — an achievement that predates the current agent wave and gives his present-day work an unusual lineage. Distributed intelligence, the discipline of coordinating many computational agents into coherent behavior, sits at the heart of both that earlier platform and the agentic architectures now being pitched to enterprise customers.
Before Sentient, he co-founded Dejima, a venture developing agent-oriented natural language technology — work that anticipated, by a wide margin, the conversational and agentic interfaces at the center of today's enterprise AI roadmaps. The throughline is difficult to miss: many of the ideas now packaged as agentic AI were fields Hodjat was already exploring when commercial AI looked nothing like it does today.
The interview is framed as a return conversation — Hodjat has appeared with Unite AI before — and it lands as agentic AI moves from research papers into enterprise procurement discussions.
For technology leaders weighing agent deployments, the conversation functions as a checkpoint. Hodjat's stated focus on AI systems designed for large-scale enterprise deployment is the operative detail: it positions the labs' research not around model novelty but around whether agentic and distributed architectures hold up under real enterprise load — the exact criterion buyers will apply when the technology reaches procurement.
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