À PROPOS DE CET ÉPISODE
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.
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États-Unis
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RECHERCHER LES ÉPISODES PASSÉS
Rechercher les épisodes précédents de Agent Sense | Agentic Workflows & Operational AI.
AUTRES ÉPISODES DANS CE PODCAST
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 Technica…
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.
Theme: As AI systems evolve from single models into networks of autonomous agents, integration is the primary bottleneck.
Core Integration Challenges:
Fragmented Tool Access
Context Loss Across Agents
Tight Coupling & Low Reusability
Lack of Standardized Communication
Integration needs sta…
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 a…
Avertissement: Le podcast et les illustrations intégrés sur cette page proviennent de Monika Aggarwal, Operational AI, IBM and Frank Chavez, Technical Architect, IBM, qui est la propriété de son propriétaire et n'est ni affilié ni approuvé par Listen Notes, Inc.
MODIFIER
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