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Deep-Dive Into Agentic Workflows, w/ Cognizant’s Head of AI
What happens when software stops just “chatting” and starts acting in the real world, across real workflows, with real consequences?
In this episode of AI-Curious, the Head of AI at Cognizant goes deep on AI agents and agentic workflows: what they are, why enterprises are investing heavily, and what it actually takes to make agent systems reliable and safe at scale. We unpack what separates an AI agent from a traditional chatbot, why “agency” changes the stakes, and how multi-agent systems can be designed to reduce risk instead of amplifying it.
We also explore concrete enterprise use cases, including agent hierarchies that coordinate across complex systems (like networks, utilities, and other operations), plus how “agentic process automation” builds on older automation models while adapting to unexpected edge cases. Finally, we zoom out to the future of work: which tasks get augmented first, why disruption is happening faster than most forecasts, and how trust in AI systems may shift over the next several years.
Guest
Babak Hodjat — Head of AI at Cognizant; leads AI lab work focused on scaling reliable, trustworthy agent systems; longtime AI builder with deep experience in applied natural language systems.
Key topics we cover
- 07:00 — What an AI agent is (and how it differs from a chatbot)
- 13:03 — State of play: what’s working, what’s not, and why “agent systems must be engineered”
- 17:00 — A practical multi-agent design pattern across telecom, power, and agriculture
- 20:28 — Agentifying rigid processes (and handling unforeseen situations)
- 24:14 — Who should deploy agents, why single “do-everything” agents are risky
- 26:34 — An open-source starting point for experimenting with multi-agent systems
- 29:12 — Guardrails: reducing hallucinations, adding redundancy, and safety thresholds
- 35:29 — Why we should use LLMs for reasoning, not knowledge retrieval
- 38:15 — The future of work: tasks, jobs, and decision-making roles shifting upward
- 41:59 — AGI, limitations, and why modular multi-agent systems may matter
- 44:57 — A prediction: we’ll delegate more than we expect as systems become more trustworthy
Follow AI-Curious on your favorite podcast platform:
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View all episodes
By Jeff WilserWhat happens when software stops just “chatting” and starts acting in the real world, across real workflows, with real consequences?
In this episode of AI-Curious, the Head of AI at Cognizant goes deep on AI agents and agentic workflows: what they are, why enterprises are investing heavily, and what it actually takes to make agent systems reliable and safe at scale. We unpack what separates an AI agent from a traditional chatbot, why “agency” changes the stakes, and how multi-agent systems can be designed to reduce risk instead of amplifying it.
We also explore concrete enterprise use cases, including agent hierarchies that coordinate across complex systems (like networks, utilities, and other operations), plus how “agentic process automation” builds on older automation models while adapting to unexpected edge cases. Finally, we zoom out to the future of work: which tasks get augmented first, why disruption is happening faster than most forecasts, and how trust in AI systems may shift over the next several years.
Guest
Babak Hodjat — Head of AI at Cognizant; leads AI lab work focused on scaling reliable, trustworthy agent systems; longtime AI builder with deep experience in applied natural language systems.
Key topics we cover
- 07:00 — What an AI agent is (and how it differs from a chatbot)
- 13:03 — State of play: what’s working, what’s not, and why “agent systems must be engineered”
- 17:00 — A practical multi-agent design pattern across telecom, power, and agriculture
- 20:28 — Agentifying rigid processes (and handling unforeseen situations)
- 24:14 — Who should deploy agents, why single “do-everything” agents are risky
- 26:34 — An open-source starting point for experimenting with multi-agent systems
- 29:12 — Guardrails: reducing hallucinations, adding redundancy, and safety thresholds
- 35:29 — Why we should use LLMs for reasoning, not knowledge retrieval
- 38:15 — The future of work: tasks, jobs, and decision-making roles shifting upward
- 41:59 — AGI, limitations, and why modular multi-agent systems may matter
- 44:57 — A prediction: we’ll delegate more than we expect as systems become more trustworthy
Follow AI-Curious on your favorite podcast platform:
Apple Podcasts
Spotify
YouTube
All Other Platforms