Loop Engineering 101 Explained | Rakesh Gohel

Loop Engineering 101 Explained | Rakesh Gohel AI agent open loops can burn 2M tokens in a single run. Closed loops fix that; here's the difference... Most people use AI agents by prompting one task at a time. Build this. Now fix that. Now write the tests. You drive every step and that makes YOU the bottleneck. Loop Engineering flips the model. You build the system that prompts the agent. It runs until the goal is met. 📌 The AI Agent Loop has 5 steps: 1. Discovery → The agent finds what it needs before acting. No guessing. No missing context. 2. Planning → It breaks the goal into clear, executable steps. Scope defined. Path set. 3. Execution → The agent does the actual work writing, analyzing, building, connecting tools. 4. Verification → It checks its output against the goal and your quality standard. 5. Iteration → fixes the gaps and runs again until the work clears the bar. 📌 Every good loop has 6 building blocks: 1. Automations → runs on a schedule, not on you 2. Worktrees → parallel agents, zero file collisions 3. Skills → project knowledge written once, read every loop 4. Plugins & Connectors → connects to PRs, tickets, Slack 5. Subagents → maker and checker are never the same agent 6. Memory → lives outside the conversation, never forgets → Single Agent vs Fleet: One agent improving its own work is enough for most tasks. A fleet of specialists is for when one brain isn't enough. → The Quality Gate: No gate = slop machine. Build it from things the agent can't argue with tests, linters, CI, type systems. Not a comment it can rationalize around. → The Self-Learning Loop: Agents forget between runs. Write lessons to a RULES.md file on disk. Every mistake caught becomes a wall forever. Your loop gets smarter every cycle. 📌 Where do you sit on the Loop Engineering Maturity scale? Level 1 — Prompter: One task at a time. Reactive. Level 2 — Operator: Runs agents manually. Hands-on. Level 3 — Loop Engineer: Designs agent lo