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
  • Integration Will Decide Enterprise AI with MCP and Agent-to-Agent
    Mar 25 2026

    Theme: As AI systems evolve from single models into networks of autonomous agents, integration is the primary bottleneck.

    Core Integration Challenges:

    1. Fragmented Tool Access

    2. Context Loss Across Agents

    3. Tight Coupling & Low Reusability

    4. Lack of Standardized Communication

      Integration needs standards.
      MCP standardizes how agents connect to systems.
      Agent to agent communication standardizes how they pass work.


      🔷 MCP connects agents to enterprise systems.
      🔷 A2A connects agents to each other so work can move across the enterprise.

    Más Menos
    5 m
  • Autonomous Databases, Where Autonomy Helps and Where It Hurts
    Feb 23 2026

    Theme: Do autonomous databases fix bad data, or do they mainly improve operational reliability? Why are organizations moving toward autonomous operations?


    In episode 3, we talked about an IT service agent that created operational noise during an outage. The AI agent acted fast, but the ownership and escalation data were wrong, so the actions were wrong.


    If the data underneath these systems is fragile, should the data layer become autonomous too? In eposide 4 we are talking about autonomous databases, and what they can and cannot do in incidents like this.


    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.

    Más Menos
    5 m
  • Why IT Service Agents Fail in Production, A Data Readiness Problem
    Feb 9 2026

    Theme: Foundations & Data Readiness. Why do agents go rogue when the information source is weak?

    This is episode three: Why IT Service Agents Fail in Production, A Data Readiness Problem. Most enterprise agentic failures are not related to the model. They are data failures. We are using a real IT service ticketing example to show why data readiness matters for agents.


    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.


    Más Menos
    3 m
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