HALO — Hierarchical Agent Loop Optimizer

HALO (Hierarchical Agent Loop Optimizer) is an RLM-based methodology for recursively self-improving AI agents. It analyzes agent execution traces and suggests changes, boosting AppWorld performance from 73.7 to 89.5 (+15.8 pts) with Claude Sonnet 4.6 — a new SOTA for agent optimization.

HALO (Hierarchical Agent Loop Optimizer) is an RLM-based methodology for recursively self-improving AI agents. It analyzes agent execution traces and suggests changes, boosting AppWorld performance from 73.7 to 89.5 (+15.8 pts) with Claude Sonnet 4.6 — a new SOTA for agent optimization.

• 01Recursively self-improves agents by analyzing execution traces
• 02Achieved +15.8 point gain on AppWorld benchmark (73.7 to 89.5)
• 03RLM-based optimization requiring no manual intervention