Agent Sense | Agentic Workflows & Operational AI Podcast Por Monika Aggarwal Operational AI IBM and Frank Chavez Technical Architect IBM arte de portada

Agent Sense | Agentic Workflows & Operational AI

Agent Sense | Agentic Workflows & Operational AI

De: Monika Aggarwal Operational AI IBM and Frank Chavez Technical Architect IBM
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You are listening to Agent Sense. Where we keep AI simple, practical, and grounded.” I am Monika Aggarwal, AI Technical Practitioner. I specialize in Operational Al and building agentic workflows grounded in clear rules, good data, and governance. I am joined by my colleague Frank Chavez. He is a Technical Architect and hands-on builder specializing in multi-agent orchestration and AI integration patterns. I bring the enterprise and operational view. Frank brings the engineering view. We keep it simple and honest. Let’s start.” Disclaimer: The views shared on this podcast are our own and do not represent IBM's viewpoint.Monika Aggarwal, Operational AI, IBM and Frank Chavez, Technical Architect, IBM
Episodios
  • Rules, Agents, Humans - A Practical Model for Agentic Workflows

    Rules, Agents, Humans - A Practical Model for Agentic Workflows
    Jan 10 2026

    This episode explores the line between deterministic business logic and autonomous agents in real business operations, using a Commercial and Investment Banking onboarding scenario to show where rules work, where agents help, and where humans must stay in control.


    I am Monika Aggarwal, AI Technical Practitioner. I build agentic workflows grounded in clear rules, good data, and governance. I am joined by my colleague Frank Chavez. He is a Technical Architect and hands-on builder specializing in multi-agent orchestration and AI integration patterns.


    I bring the enterprise and operational view. Frank brings the engineering view.

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    3 m
  • Brains and Guardrails: What Makes an AI Agent Enterprise-Ready?

    Brains and Guardrails: What Makes an AI Agent Enterprise-Ready?
    Dec 16 2025

    Enterprises often stop at building a clever prototype. But when that agent touches real systems, the question changes from Can it run? to Can we trust it?
    That is the gap between experimentation and production.
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    2 m
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