Toward Self-Evolution-Ready Workflow Harnesses: A Reversible Migration Path and Convertibility Taxonomy for Expert LLM Pipelines
arXiv:2606.24598v1 Announce Type: cross 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 (
The rapid deployment of foundational LLMs has accelerated the need to integrate them into existing enterprise workflows, creating a critical challenge for migration and scalability.
This research provides a structured pathway for enterprises to transition static 'LLM + script' workflows into more adaptive and auditable agentic systems, unlocking greater AI utility and reducing technical debt.
Enterprises can now envision a reversible, staged migration from rigid, expert-validated LLM workflows to flexible, self-evolving pipelines, rather than being forced into greenfield agent development.
- · Enterprise software vendors
- · Consulting firms specializing in AI integration
- · Organizations with significant legacy AI projects
- · Developers working on LLM operations
- · Companies unable to adapt legacy systems
- · Providers of purely static AI workflow tools
Existing expert LLM workflows will gain new capabilities for adaptation and autonomous evolution.
This will accelerate the adoption of agentic systems in regulated and mission-critical enterprise environments due to enhanced auditability and controlled migration.
The increased sophistication of enterprise AI agents could lead to significant reductions in white-collar operational costs and accelerate business process automation across industries.
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Read at arXiv cs.AI