| name | Self-Evolving Systems |
| description | Automated harness evolution using MetaHarness propose-evaluate-log-iterate loops, Karpathy autoresearch patterns, and the Bitter Lesson applied to agentic orchestration |
Self-Evolving Systems
The harness around the model matters as much as the model itself. A 6x performance gap exists between the best and worst harness for the same weights. Hand-engineering harnesses is the status quo. It shouldn't be.
Core Capability
Design, run, and audit self-improvement loops for any agentic system component — workflows, prompts, retrieval logic, memory management, orchestration patterns. Uses the MetaHarness methodology: a coding-agent proposer that inspects prior code + execution traces, diagnoses failure modes, proposes targeted edits or full rewrites, and iterates until the system converges on Pareto-optimal performance.
This is not self-correction (recovering from errors). This is self-evolution (permanently improving the system).
What This Skill Produces
| Workflow | Output |
|---|
| Self-Evolve | Full MetaHarness-style evolution loop on any Antigravity component |
| Harness Evolve | Focused evolution of a specific workflow's prompts, flow, and gates |
| Auto-Experiment | Karpathy-style experiment runner with git-branch isolation |
| Evolution Audit | History inspection, Pareto frontier, regression detection |
| Skill Anneal | Self-annealing applied to skill prompts/workflows via past failures |
| Proposer Sprint | Time-boxed, fixed-iteration improvement sprint |
| Evolution Status | Dashboard of all active/completed evolution loops |
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