Toward Self-Evolution-Ready Workflow Harnesses: A Reversible Migration Path and Convertibility Taxonomy for Expert LLM Pipelines

2026-06-15

AI Summary

This work addresses the limitations of current expert-validated “LLM+script” workflows, which lack adaptability, cannot dynamically evolve based on feedback, and offer no effective pathway toward agent-based architectures. To overcome these challenges, the paper proposes a reversible “Strangler Fig” migration framework that transforms static workflows into composable, typed, and auditable stages. It introduces a three-tier convertibility classification—A/B/C—to enable dynamic routing and progressive evolution. This approach uniquely facilitates a smooth, structured transition from legacy LLM workflows to self-evolving agent systems while providing an assessment capability to determine evolutionary readiness.

Abstract

While expert-validated "LLM + script" workflows deliver significant value, they remain static: they encode hard-won domain knowledge yet fail to adapt execution based on feedback. Existing agent research predominantly targets greenfield agents and synthetic benchmarks, leaving the migration of active legacy workflows unresolved. To bridge this gap, we present a reversible, Strangler-Fig migration path that refactors legacy workflows into composable, typed, and auditable stages. Central to this framework is a three-tier convertibility taxonomy (A/B/C), implemented as a routing stage within the system harness, which diagnoses a workflow's readiness and routes it accordingly.

Problem

• LLM workflows
• workflow migration
• self-evolution
• legacy systems
• adaptability

Innovation

• reversible migration
• convertibility taxonomy
• LLM workflows
• Strangler-Fig pattern
• self-evolution-ready