While You Were on Vacation, AI Infrastructure Got a Vocabulary Update
What the hell are RLMs? What’s Ralph Wigguming? Welcome back. If you’re lucky, you only have to catch up on 2700 unread emails AI’s entirely new jargon layer.
While everyone was arguing about Claude vs ChatGPT over the holidays, engineers were quietly building infrastructure that actually matters. Here's the cheat sheet:
Context Graphs
= GPS with real-time traffic vs. a paper map Not just what your company knows, but how work actually happens. That engineering teams from startups hate your 47-page documentation requirements. That Sarah in Finance needs three reminders before Q3 closes. The unwritten rules that determine whether AI suggestions work in your actual culture vs. just in demos.
Agent Harnesses
= Autopilot safety systems The control system preventing your AI from veering off course after 100 decisions. Without it, agents suffer "model drift"—like drivers getting drowsy on a long highway. Good harnesses keep things on track with guardrails and checkpoints. Bad ones let agents declare the destination reached when you're still 90% away.
Agent Orchestration
= A restaurant kitchen during dinner rush Coordinating specialized stations (grill, sauté, pastry) instead of one overwhelmed cook. Someone preps, someone cooks, someone plates, someone expedites. Companies doing this right report 73% improvement in task completion.
MCP (Model Context Protocol)
= like USB for AI One protocol to connect any AI to any data source. Before: custom code for every integration. After: plug and play. Announced November 2024. OpenAI and Google adopted it by March. Now the de facto standard.
Context Engineering
= Redesigning the library, not asking better questions Prompt engineering is dead. Context engineering is infrastructure—how you assemble, govern, and refresh what information reaches your AI. Organizations treating it like inventory management see measurably better results than those treating it like a prompt file.
Ralph Wiggum-ing
= Training until you get it right, not getting it right on the first try Named after the Simpsons kid who never stops trying despite constant confusion. Run your AI coding agent in a loop—it works, tries to exit, gets blocked, sees what it just did, tries again. Each iteration learns from failures. It's how developers are shipping entire repos overnight. Persistence beats perfection.
RLMs (Reasoning Language Models)
= The chess player who thinks ahead vs. the speed chess player Models like o1 and DeepSeek-R1 that "spend more time thinking" before answering. They don't just pattern-match—they plan multiple steps ahead, explore different solution paths, revisit earlier reasoning. Solve 50-80% of advanced math problems vs. under 30% for regular models. Think slow and careful vs. fast and first-guess.