Aaros Evolve.
Can an AI agent learn from its failures and become more reliable?
A self-improving reliability system for AI agents. It turns failures into changes you can test and evaluate.
A self-improving reliability system for AI agents. It connects records of agent behavior, evaluations, and controlled changes into a learning loop: investigate a failure, test an improvement, and check whether the agent actually performs better. I’m building it to turn an agent’s experience into measurable improvements in how it works.
The learning loop
The loop starts with a question about a particular failure. A proposed change needs an evaluation that can catch that failure again. Comparing the results is how I want to decide whether to keep the change.
What I’m building
I’m building the system around that loop: what happened, what changed, and whether the change helped. The diagram shows the intended workflow. My next question is what evidence should be required before keeping an improvement.