Operations | Monitoring | ITSM | DevOps | Cloud

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.

Welcome to “Elephant in the Room – Presented by Aiven.” In this live series, we dig into the real-world challenges developers face when building modern applications on PostgreSQL, the elephant everyone depends on but few fully leverage. Episode 2: Join Jay Miller, Staff Product Advocate at Aiven, and special guest Alexis Roberson – senior developer educator, and feature flag enthusiast – as they explore how developers can use feature flags to ship faster, reduce risk, and create better user experiences with PostgreSQL-backed applications.

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

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.