ABOUT THIS PODCAST 🔗
You are listening to Agent Sense. Where we keep AI simple, practical, and grounded.
I am Monika Aggarwal. I specialize in Operational AI and in building agentic workflows grounded in decisions, 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.
I am Monika Aggarwal. I specialize in Operational AI and in building agentic workflows grounded in decisions, 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.
Update frequency:
every 24 days
Average audio length:
7 minutes
Guest interviews
English
United States
14 episodes
since Dec. 16, 2025
episodic
LATEST EPISODE 🔗
𝐘𝐨𝐮𝐫 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐝𝐞𝐭𝐞𝐜𝐭𝐞𝐝 𝐭𝐡𝐞 𝐢𝐧𝐜𝐢𝐝𝐞𝐧𝐭. 𝐖𝐡𝐲 𝐝𝐢𝐝 𝐢𝐭 𝐬𝐭𝐢𝐥𝐥𝐭𝐚𝐤𝐞 the Engineers 𝐡𝐨𝐮𝐫𝐬 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 root cause?
Engineering teams have dashboards, alerts, logs, and traces. Yet finding theroot cause of a production issue can still take hours.
Frank Chávez and I spoke with Viraat Das, Cofounder and CEO of E…
SEARCH PAST EPISODES
Search past episodes of Agent Sense | Agentic Workflows & Operational AI.
PREVIOUS EPISODES 🔗
Your AI agent works, but can you prove its business value?
As enterprises invest in Agentic AI, AI automation, and autonomous AIagents, many struggle to demonstrate measurable ROI. The challenges oftenbegin before development, with unclear business requirements and poorlyunderstood workflows.
In th…
𝐀𝐠𝐞𝐧𝐭 𝐒𝐞𝐧𝐬𝐞 𝐢𝐬 𝐚 𝐬𝐡𝐨𝐫𝐭 𝐩𝐨𝐝𝐜𝐚𝐬𝐭 𝐨𝐧 𝐦𝐚𝐤𝐢𝐧𝐠 𝐀𝐈 𝐬𝐢𝐦𝐩𝐥𝐞, 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥, 𝐚𝐧𝐝 𝐠𝐫𝐨𝐮𝐧𝐝𝐞𝐝
An AI agent can be 95% accurate and still fail thebusiness. It can give the right answer and still take too long, use the wrong tool, cost too much, or fail to complete the task.
Frank Chávez and I talk with Jay Migliaccio,…
Season 2, Episode 1 - Agent Memory Is More Than RAG
AI agents can access huge amounts of enterprise data, yet they often forget the context that matters.
In this episode of Agent Sense, Monika Aggarwal and Frank Chavez talk with Ran Aroussi, founder and lead architect of MUXI, about what it take…
Most AI agents wait for a person to open a chat and give them a prompt. Ambient agents work differently. They listen for events across enterprise systems and start working when something changes.
In this episode of Agent Sense, Monika Aggarwal and Frank Chavez discuss what makes an agent ambient an…
AI can summarize a meeting. Enterprise work starts after the summary.
In this episode, Monika Aggarwal speaks with Artem Koren, co-founder and Chief Product Officer of Sembly AI, about how meeting context can move beyond notes and trigger real work.
They discuss how AI can support follow-ups, workf…
Agent Sense — Episode 8: Governed Catalog of AI Assets
Enterprise AI agents does not fail from lack of ideas. They fail from agent sprawl: duplicate builds, unclear ownership, no way to know what is safe to reuse. Jyotsna Narayanan, Principal Product Manager for watsonx Orchestrate at IBM, joins ho…
The episode is about hashtag#9minutes long. In this episode, my co host Frank Chávez and I are joined by Vitalii Duk from Dynamiq to discuss how agents are moving from answering questions to doing work across tools, systems, and runtime environments.
“𝑪𝒉𝒂𝒕 𝒎𝒆𝒂𝒏𝒔 𝒂𝒏 𝒂𝒈𝒆𝒏𝒕 𝒄𝒂𝒏 𝒂𝒏𝒔𝒘𝒆𝒓 𝒚𝒐𝒖𝒓 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏. 𝑪…
Episode 6 of Agent Sense continues from Episode 5, where we talked about MCP, A2A, and enterprise integration.
In this episode, Monika Aggarwal and Frank Chávez discuss why enterprise agents need governed access to core systems before they can scale in production.
MCP helps agents connect to tools…
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…
Disclaimer: The podcast and artwork embedded on this page are from Monika Aggarwal, Operational AI, IBM and Frank Chavez, Technical Architect, IBM, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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