Agentic AI

Production AI APIs: OAuth 2.1, Gateway Rate Limiting & Prompt Versioning Running AI APIs in production for the past 18 months has produced three lessons that I did not find in any “getting started with LLMs” tutorial. They emerged from incidents, postmortems, and that specific kind of 2 AM Slack message where a word you never wanted to see — “silent,” as in “silent failure” — appears in a production context. ...

Autonomous Hybrid-AI Pipeline: Cron to State-Machine Production AI content pipelines need deterministic orchestrators, multi-tier memory systems, and cost-aware model routing to handle automated ingestion reliably. Replacing monolithic background jobs with event-driven agents gives resilient execution, zero-idle resource usage, and stricter output verification. This post covers four pieces of that architecture: Topology & Orchestration: Master-worker agent swarms managed by explicit state machines. Memory System Architecture: Working memory (context window), short-term memory (Redis session), and long-term memory (Vector/Graph RAG). Tool Calling & MCP: Protocol-driven tool execution via Model Context Protocol. AgentOps & Governance: Tracing, fallback cascades, evaluation gates, and hardware Wake-on-LAN power optimization. 1. Agent System Topology & State Machine A resilient pipeline replaces stateless cron scripts with an explicit Finite State Machine (FSM). By encapsulating pipeline operations within state transitions, every step—from hardware boot to scraping, filtering, and drafting—is recorded with atomic rollback safety. ...