Technology.
Strategy.
Intelligence.
Sebastian Maniak on running AI agents in production — agentgateway, kagent, MCP, A2A, and the Kubernetes plumbing that keeps them fast, observable, and safe.
One-Script Deployment: agentgateway + Self-Hosted Langfuse on Kubernetes for LLM Cost Analysis
Running production-grade LLM gateways with full observability usually involves many manual steps across Helm charts, CRDs, tracing configuration, and UI key management. The agentgateway-demos/09-k8s-langfuse demo …
Read the article →Latest writing
View the full archive →agentgateway Standalone → Langfuse: Direct OTLP Tracing (No Collector)
I’ve already covered the production OTel Collector pattern for shipping agentgateway traces to Langfuse. This is the opposite end of the spectrum: the …
Langfuse Integration with agentgateway (OTel Collector Pattern) for cost controls, observability
agentgateway emits rich OpenTelemetry traces for every LLM request, tool call, and policy decision. This guide shows the production-grade way to forward those …
Self-Hosted Langfuse with Docker + kagent Integration
Running Langfuse as a self-hosted Docker stack gives you full control over your LLM observability data. This guide shows how to deploy it and integrate it with …
Running kagent on a kind Cluster with a Local vLLM + Qwen3 Backend
Running kagent on a kind Cluster with a Local vLLM + Qwen3 Backend By Sebastian Maniak Most kagent walkthroughs assume you’re pointing at OpenAI or …
Proxying All Your LLM Traffic Through agentgateway with Grok Build
Introduction If you’re like me, your AI tooling has probably grown into a sprawling mess of API keys: Claude Code reads .claude/.env, Cursor uses OpenAI …
How To: Connect Claude Code & Codex Through agentgateway (Subscription + API Key)
Introduction You’ve got Claude Code running on your laptop. You’ve got Codex open in a tab. Both are just AI coding tools, right? Wrong. Behind the …