Description
Making agents improve usually means collecting data and fine-tuning, costly and slow. Meta-Agent turns execution traces into persistent improvements to the system around a language model, without weight updates.
A fast loop refines prompts, tools, hooks, sub-agents and control flow, while an experimental slow loop improves the proposer skill itself.
No fine-tuning:Weights untouched.
Fast loop:Harness refined.
Slow loop:Proposer improved.
Trace learning:From execution.
A fast loop refines prompts, tools, hooks, sub-agents and control flow, while an experimental slow loop improves the proposer skill itself.
Features
No fine-tuning:Weights untouched.
Fast loop:Harness refined.
Slow loop:Proposer improved.
Trace learning:From execution.
