Why AI agents repeat the same mistakes
You correct an AI agent, it fixes the issue, and three sessions later it makes the same mistake again. This is not a model defect; it is a memory architecture problem. The correction lived in a conversation that ended, and nothing durable captured the lesson. Without a place for feedback to persist and be validated, every session relearns the same things.
Corrections evaporate at session end
A correction is context: "do not mock the database here," "this file owns that state," "we settled this debate already." When the session ends, that context is gone unless something captured it. The next run starts without it and walks into the same wall.
Why more memory alone does not fix it
Storing every correction is not enough, because not every correction stays true and a flood of unverified notes is its own problem. The fix is durable feedback that is structured and validated: the lesson is recorded, the reason is recorded, and it is checked against the current code before it is reapplied, so a stale correction does not become a new mistake.
Capturing the why, not just the what
A correction without its reason is brittle. "Do not do X" recorded alone cannot handle the edge case where X is actually correct. Recording why the correction was made lets a future session judge whether it still applies, instead of following it blindly.
Durable feedback ends the loop. A correction that is captured with its reason, and validated before reuse, does not have to be given twice.
How Avorelo helps
Avorelo captures the outcome of each run as a structured receipt: what was changed, what was validated, and what was learned. That record carries forward as context for the next session, so a correction made once does not evaporate at session end. Because the context is validated against the real files before reuse, durable feedback does not turn into a stale instruction.