Decoding the new AI lingo: Loops, harnesses, squads, hill climbing… oh my!

It might be overwhelming to see all of the new vocabulary popping up in software development these days thanks to AI tools introducing them… all the time. Some of this new vocab describes useful patterns that people are newly pursuing, others are just fancy names on top of things that already exist, and some are still actively being defined as we speak. In our latest episode of the GitHub Podcast , Marlene Mhangami, GPS, and I talked through some of the AI terms developers are learning right now: loop engineering, Ralph loops, squads, harness engineering, hill climbing, forward deployed engineers, closed models, open weights, and open source models. If you’re a reader instead of a listener, here’s a guide to what those terms mean, why they matter, and how to think about them. Listen to the full episode below! 👇 Loop engineering: Moving beyond one-shot prompts Loop engineering is the practice of designing repeatable systems around agents, instead of manually prompting them for one task at a time. A simple example: instead of asking an agent every morning to review new issues, summarize them, and propose fixes, you create a loop that runs on a schedule. That loop might fetch issues, pass them to an agent, validate the output, and escalate anything that gets stuck. It’s a glorified AI-native cron job. Ralph loops: The brute-force cousin of loop engineering A Ralph loop is one implementation of this “loop” concept: you give an agent a detailed task, often from a product requirements document or spec, and have it keep working until the job is done. That can be useful, especially for breaking down large tasks into repeated plan-act-check cycles. But, on the other hand, it can also be expensive and inefficient because every iteration uses more tokens, more context, and more compute. Loop engineering aims to make this pattern more structured, so you’re not caught asking an agent to “try again” all the time. A well-designed loop adds primitives like skills, observability, va