https://toddschiller.com/2026-07-07T00:00:00-04:00Human ✘ Artificial Intelligence2026-07-07T00:00:00-04:002026-07-07T00:00:00-04:00Todd Schillertag:toddschiller.com,2026-07-07:/blog/loop-engineering-compounding.html

Loop engineering for AI agents should raise the floor on each iteration by creating tools and correcting instructions.

<p>Loop engineering for AI agents should be about compounding, not repetition.</p> <p>A weak work loop does the same thing every pass: trigger, execute, review.</p> <p>A strong work loop leaves the system better than it found it.</p> <p>Sure, many harnesses now automatically improve via agent memory. However, that's leaving your work to chance: memory creation is inconsistent, and false memories degrade performance.</p> <p>The better way is to edit the loop itself:</p> <ul> <li>Skills/Instructions: conventions, assumptions, examples, failure modes</li> <li>Tools: scripts, workflows, fixtures, test harnesses</li> </ul> <p>With each run, the loop becomes more capable, more reliable, and more cost-effective.</p> <p>What are your go-to strategies for compounding?</p> <!-- markdownlint-disable MD013 --> <img loading="lazy" decoding="async" src="https://toddschiller.com/assets/images/loop-engineering-compounding/loop-engineering-compounding-landscape-ai-whiteboard-2400.png" alt="Whiteboard-style diagram titled Agent loops should compound. The left panel shows a repeating routine that starts from the same place each time: Trigger, Execute, Review. The right panel shows a compounding routine where loop 1, loop 2, and loop 3 climb upward on a system-improves axis, with orange step-up arrows labeled Refine and gains labeled Task done, plus instruction, and plus tool."> <!-- markdownlint-enable MD013 -->