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.

Alert fatigue usually isn’t caused by one thing. It’s the accumulation of thresholds that are slightly too sensitive, alerts that fire during known maintenance windows, and historical patterns that nobody has the tools to review easily. Fixing it requires better visibility into how alerts actually behave over time, and a way to test changes before they hit production. We’ve shipped three improvements to alerting in Netdata that address different parts of this problem.