triple-loop-learning

What problem does it solve?

Continuous, self-improving orchestration of an agentic system across multiple sessions to identify workflow friction, propose improvements, and validate them against objective benchmarks.

Core Features & Use Cases

• Autonomous outer-loop, mid-loop, and inner-loop orchestration for hypothesis generation, strategic planning, and tactical execution.
• Headless evaluation with automated scoring to promote only improvements that meet objective criteria.
• Guarded, production-grade experimentation for durable, auditable improvements in agent systems.

Quick Start

Provide an initial workflow prompt and allow the meta-learning orchestrator to autonomously hypothesize, plan sub-tasks, execute mutations, and evaluate results.