Operations | Monitoring | ITSM | DevOps | Cloud

I've been building a multi-agent research system. The idea is simple: give it a controversial technical topic like "Should we rewrite our Python backend in Rust?", and three agents work on it. An Advocate argues for it, a Skeptic argues against, and a Synthesizer reads both briefs blind and produces a balanced analysis. Each agent has its own model, its own tools, its own system prompt. It worked great in testing. Then I noticed the Synthesizer kept producing analyses that leaned heavily toward one side.

12 proven Google Cloud cost optimization strategies to cut GCP spend by 30–70%. Covers CUDs, rightsizing, labeling, and GCP pricing vs AWS.

Traditionally, achieving deep visibility into distributed systems required significant trade-offs in engineering time. Collecting meaningful application metrics and traces required teams to embed language-specific agents, modify source code, or manage complex library dependencies across every service.

Last month, the VictoriaMetrics team gave a talk on retroactive sampling at KubeCon Europe 2026. By writing this blog post, as a transcript of the session, we want to explain how retroactive sampling reduces outbound traffic, CPU, and memory usage in the data collection pipeline significantly compared to tail sampling in OpenTelemetry.