O TYM ODCINKU
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 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.
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TRANSKRYPCJA 🔗
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WYSZUKAJ MINIONE ODCINKI
Wyszukaj minione odcinki Agent Sense | Agentic Workflows & Operational AI.
INNE ODCINKI W TYM PODCASCIE
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 …
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…
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…
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.
Zastrzeżenie: Podcast i grafika osadzone na tej stronie pochodzą z Monika Aggarwal, Operational AI, IBM and Frank Chavez, Technical Architect, IBM, który jest własnością jego właściciela i nie jest powiązany ani wspierany przez Listen Notes, Inc.
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