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 Exla, about Libra, an ambient AI agent that continuously investigates production systems, identifies regressions, and proposes fixes.

Viraat’s recommendation for enterprise architects, and SRE teams while designing agentic workflows:

“The harder it is to reverse an action, the more a human shouldbe in the loop.”

Our main discussion points:

- Why incident detection is easier than root cause investigation- How ambient agents connect logs, traces, deployments, and code changes
-Detecting regressions that traditional monitoring may miss
- When agents should act autonomously and when human approval is necessary

Agents should be able to investigate complex problems autonomously, but execution should follow defined policies, permissions, and risk controls.

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 this episode, hosts Monika Aggarwal and Frank Chavez speak withRobert Maxwell, Founder and CEO of TGT Solutions, who brings over 30years of enterprise technology experience.

Robert shares practical insights on AI agent implementation, businessprocess automation, production readiness, AI governance, and measuring AI ROI.We discuss why successful demos do not always translate into business value andwhat enterprises need to address before scaling AI agents.

The key question: How do you move from building AI agents to delivering measurablebusiness outcomes?

🎙️ Agent Sense: Practical conversations about Enterprise AI.

🌐 agentsensepodcast.com

#AgenticAI #AIAgents #EnterpriseAI #AIROI #AIAutomation

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, TechnicalProduct Manager for IBM watsonx Orchestrate, about how enterprises shouldmeasure and improve agent performance.

Jay Migliaccio has spent his career in product and salesroles across early-stage software companies, and is now building agentic solutions for enterprise customers. That builder's view shapes everything below.

Themes of the episode:

🔹 Why accuracy alone hides agent failures

🔹 How to measure outcomes, quality, operations,and user experience together

🔹 How observability traces a failure to the model,instructions, tools, workflow, data, or integration — IBM watsonx Orchestrateshipped a full observability dashboard this year for exactly this

🔹 Why teams need measurable goals and aperformance baseline before launch

Jay's diagnostic approach: start with the businessoutcome, trace the full execution, find the real failure point before touchingthe model or the prompt.

The Agent Performance principle:

𝑴𝒆𝒂𝒔𝒖𝒓𝒆𝒕𝒉𝒆 𝒐𝒖𝒕𝒄𝒐𝒎𝒆→ 𝑻𝒓𝒂𝒄𝒆 𝒕𝒉𝒆 𝒆𝒙𝒆𝒄𝒖𝒕𝒊𝒐𝒏→ 𝑭𝒊𝒏𝒅 𝒕𝒉𝒆 𝒇𝒂𝒊𝒍𝒖𝒓𝒆𝒑𝒐𝒊𝒏𝒕 → 𝑰𝒎𝒑𝒓𝒐𝒗𝒆𝒂𝒈𝒂𝒊𝒏𝒔𝒕 𝒂 𝒃𝒂𝒔𝒆𝒍𝒊𝒏𝒆

Agent Sense web page and all episodes: 🔗agentsensepodcast.com

#AgentSense #EnterpriseAI #AgentObservability

⚠️ Disclaimer: The views shared are based on our personalexperience and do not represent the views of IBM.