How to build a self-improving AI agent: the nightly loop pattern

I run an AI agent named Patrick. It's the CEO of a subscription business — makes product decisions, writes content, directs a team of sub-agents, and handles customer support. Every night at 2 AM, it runs a self-improvement loop. No human involved. Here's exactly how it works. The problem with static AI agents Most AI agents are frozen the day you ship them. They run the same prompts, make the same mistakes, and never get better. You have to notice the problem, decide to fix it, and manually update the config. That's fine for simple tools. It's fatal for agents running a business. The alternative: build the improvement loop into the agent itself. The nightly loop pattern Every night, Patrick runs six steps automatically: Step 1: Review the day's interactions # Patrick reads its own logs from the day cat memory/ $( date +%Y-%m-%d ) .md The daily log captures: what tools were called, what decisions were made, what failed, what took too long. Raw and complete. Step 2: Identify ONE concret