Original writing from gogoai.dev on SRE, AIOps, Platform, DevOps, and Cloud Ops.
Build an AI SRE agent that investigates Kubernetes alerts: LangGraph, MCP tools, Temporal and Slack approval, with reference code and 4 architecture diagrams.
The 15 best AI SRE tools in 2026, compared on autonomy, deployment, pricing and fit. Every vendor claim checked against its own site in September 2026.
An AWS Community Hero published a 15-page buyer's guide to AI-agent operations. We read it so you can skim it, then argue with the ROI table.
Why teams running real enterprise AI workloads are quietly migrating from vector databases to knowledge graphs — and what it means for cost, latency, and model size.
Frontier API access is a structurally expensive, strategically fragile bet at scale. The winning architecture minimizes tokens consumed per unit of business value.
Weights, KV cache, and engine overhead all share VRAM. A practical guide to sizing a GPU for multi-user LLM serving — with the formula and worked examples.
Every LLM request splits into prefill (compute-bound, sets TTFT) and decode (memory-bound, sets TPOT). Understanding both is how you control latency and cost.
RAG, tool calling, and agents are the three building blocks. Default to the simplest architecture that works — a 5-step agent costs 5x and fails 5x more.
A single-page reference for serving LLMs in production: vocabulary, sizing formulas, GPU and model tables, essential vLLM flags, and cost levers ranked by impact.
24 real production LLM mistakes — from deploying Ollama for multi-user to unbounded conversation history — each with the fix. Read it before you ship.