The Data Exchange with Ben Lorica Why Traditional Observability Falls Short for AI Agents
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Jan 22, 2026 Lior Gavish, CTO and co-founder of Monte Carlo Data, dives into the shift from data observability to agent observability. They explore how AI is transforming data teams into data-and-AI teams and discuss the broad adoption of agents across industries. Lior emphasizes the importance of capturing granular telemetry to understand complex agent decisions and the challenges of measuring output quality with traditional methods. He introduces automated troubleshooting agents and highlights the critical role of observability for optimizing AI performance.
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Data Teams Become Data+AI Teams
- Data teams have shifted from analytics-heavy work to building and operating AI agents at scale.
- This transition requires new observability approaches that span data inputs and agent outputs.
Customers Across Industries Run Thousands Of Agents
- Monte Carlo's customers span tech, manufacturing, media, and education and some run thousands of agents.
- Lior reports that 5-10% of organizations have scaled agents to production but many more are about to.
Use Observability Agents To Automate Monitoring
- Automate monitoring workflows by using observability agents to reduce manual debugging overhead.
- Let observability agents triage issues so human teams focus on remediation and improvement.