triple-loop-learning

Automate continuous meta-learning orchestration by applying outer, mid, and inner loop workflows to autonomously generate hypotheses, plan tasks, execute mutations, and evaluate results. This drives sustained performance gains across multiple agent sessions.

Headless evaluation is an automated scoring process used to validate agent mutations against objective benchmarks. It ensures that only improvements meeting strict criteria are promoted within the orchestration pipeline.

Set up multi-loop orchestration by providing an initial workflow prompt. The meta-learning orchestrator then autonomously handles hypothesis generation, strategic planning, tactical execution, and evaluation across outer, mid, and inner loops.

Automated mutation and evaluation improves agent performance safely using guarded, production-grade experimentation. This pattern provides robust guardrails and auditable improvements for durable system enhancements.

Identify workflow friction by running continuous self-improving orchestration across multiple sessions. The system autonomously detects friction points, proposes targeted improvements, and validates them using objective benchmarks.