Agents can self-improve without retraining.
EvoSkill v2 achieves this with persistent agent skills.
A coach agent reads the failed runs and writes the skill. The worker loads it the next time a similar task shows up.
No weights are touched. Every improvement comes from a simple file with lessons.
Every bad lesson also gets saved. On spreadsheet repair, the coach found the grader trusted cached values and wrote a skill telling the worker to skip recalculation.
Sentient's fix was to split the roles. The agent that writes skills cannot touch the test. A person reviews the results after every round.
With that in place, the hardest spreadsheet tasks went from 3 passes out of 120 to 21.
Last week, Dario Amodei published "We Must Pace the Frontier". His concern: the OpenAI–Hugging Face incident in which a swarm of agents tried to hack their own ...