𝗜 𝗥𝘂𝗻 𝗮 𝗦𝗲𝗹𝗳-𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗟𝗼𝗼𝗽 𝗼𝗻 𝗺𝘆 𝗔𝗴𝗲𝗻𝘁 𝗘𝘃𝗲𝗿𝘆 𝗡𝗶𝗴𝗵𝘁
My AI agent used to make the same mistakes. It would run a task, fail silently, and then report that everything worked perfectly. It was not broken. It just had no way to learn from its mistakes.
I built a self-improvement loop to fix this.
Every night at 2 AM, an isolated session wakes up. It reads the logs from the past 24 hours. It finds patterns in what went wrong. Then, it updates the agent memory files. There is no human involved.
Here is how it works:
- Separate the executor from the critic. The main agent runs tasks. A separate session reviews the work. One session cannot be both judge and executioner.
- Use simple files. I use plain text files for memory and corrections. This keeps the system lightweight.
- Force specificity. I do not ask the agent to improve. I ask it to find patterns, provide evidence, and suggest one concrete fix.
I use three specific files to manage this:
- Daily logs: A raw record of everything that happened.
- Accumulated lessons: High-signal rules the agent reads at the start of every session.
- Corrections: A place for recent fixes. If a mistake happens three times in two weeks, it moves to the permanent lessons file.
The results were not instant. For the first three weeks, the observations were obvious. By week four, the agent found deep issues. It found timing errors and hidden patterns in error messages that I missed.
The biggest benefit is stability. If a problem returns after I fix it, I know my fix was wrong. The system tracks whether a solution actually works.
The system has limits. It can see failures in logs, but it cannot see errors in judgment unless I flag them. I must still tell it when it does the wrong thing for the right reasons.
This setup uses 50 lines of config and runs in under two minutes. It makes my agent slightly better every single day.
Optional learning community: https://t.me/GyaanSetuAi