Artificial intelligence concepts and practical AI systems engineering.
OpenAI and Anthropic both document outages, overload codes, and best-effort availability. The survival kit: LLM-aware circuit breakers, same-model-first fallback chains, and degraded modes designed before you need them.
Picture one agent, one context window, one serial loop — until it hits 180K tokens and silently drops 37 of the 40 services it was asked to audit. The orchestrator pattern fans work out to isolated sub-agents in parallel, then synthesizes. Here's the backend, in compiling Go.
Cloudflare put its 2,500+ API endpoints behind one MCP server with two tools, search + execute, and cut input tokens by 99.9%. The pattern in Go.
AI agents calling tools via MCP create new attack surfaces: prompt injection through tool responses, credential leakage, and unauthorized execution.
Compare pgvector, Pinecone, Weaviate and Milvus (plus Qdrant and LanceDB briefly) on operational fit, scaling and pricing — with real code and each vendor's own documentation.
Production LLM API patterns: streaming, function calling, retries, token budgets, cost optimization, and observability for backend engineers.
Build RAG systems that work in production: chunking strategies, embedding selection, pgvector ops, and retrieval quality evaluation.
Spring AI 1.1 deep-dive: production RAG pipeline with PII scrubbing, circuit breakers, Micrometer observability, and answer evaluation.