Multi-Agent Systems Are Distributed Systems With a Confidence ProblemAgentic systems inherit every classic distributed systems failure mode plus one they don't have a name for yet: an agent can fail completely and still sound like it succeeded. A look at which reliabil2AASep 4 · 6 min readalexeygolev.hashnode.dev
The Limits of One Model Per Pod ML PlatformEvery engineering platform is built around trade-offs. For several years the model serving platform followed a simple idea: one model, one Kubernetes pod. It was easy to understand, inexpensive to opeJul 8 · 3 min readalexeygolev.hashnode.dev
The Most Expensive Word in AI Agents Is “Parallel”I was using a knowledge assistant connected to the systems my team actually works in: Slack, Confluence, Jira, GitHub, internal catalogs, monitoring. It answered questions well. Then I looked at the tJul 2 · 4 min readalexeygolev.hashnode.dev
Tracing in Production: When 1–150ms Turns Into 700ms We run machine learning models in production serving real-time traffic. When our managed serving platform introduced built-in MLflow tracing, we wanted to enable it. Tracing promised visibility into iMay 18 · 4 min readalexeygolev.hashnode.dev
The Hidden Cost Curve of “One-Click” ML ObservabilityThe ask was reasonable. We had a high-volume ML serving endpoint and we, engineers and data scientists, wanted visibility into what requests and responses looked like in production. The managed ML serMay 13 · 4 min readalexeygolev.hashnode.dev