MerryCo

Agentic learning with a frozen agent is difficult. Agents can't reliably extract signal (cause of errors), and world-drift (the outcomes change due to the world of input changing) make it difficult to rely on old memories. For that I present my custom agentic harness-learner. It works by accumulating errors from agents that finish a job (in this case financial analysis), categorizing them and clustering them to learn shape, and then, after some accumulation uses an external oracle (in this case a strong agent that knows facts of the world) to create the rules. The memory corpus then has every agent use an adaptive hook-latch mechanism (think attention) that allows relevant rules to input case be injected into the model. Each rule features a full lifecycle, so rules can change and be forgotten