Operations | Monitoring | ITSM | DevOps | Cloud

Front‑end bugs are notoriously hard to reproduce. By the time an error shows up in your monitoring tool, the most important context is already gone: *what the user actually did*. By letting an AI agent like Copilot analyze Rollbar's session replay data directly, teams can move from *“something broke”* to *“here’s exactly why it broke”* in minutes, not hours.

Building your own agent that references complex documentation can help save time and improve the end user experience.

Clint Sharp explains why traditional schema-on-read systems cannot handle the query loads of the future. Agentic telemetry requires a 360-degree view, but structuring data only when you read it is too slow for AI-driven workloads. The solution is using LLMs to drive the cost of building parsers to near zero. Tools like Copilot Editor allow teams to map data to OCSF instantly, effectively building factories of parsers to handle the scale of agentic AI.

Clint Sharp demonstrates how Cribl Search leverages AI to streamline incident investigation. Starting from a Slack channel, the AI builds an interactive notebook, analyzes order processing logs, and identifies suspicious traffic spikes. It connects high CPU usage to a recent Jenkins deployment, hypothesizing a supply chain attack, and ultimately recommends a rollback. This isn't a far off concept. It is the future of operations arriving right now.

Learn how to capture and use response data from network calls to build smarter and more reliable AI-driven tests. This walkthrough covers the full workflow from configuring user actions to extracting backend responses, validating data, and creating dynamic test flows. You will also see how response data improves debugging visibility and supports data-driven automation. The video includes Ideal for developers, testers, and platform engineers looking to improve the accuracy and resilience of AI-powered test suites.