Why Migration Workflows Now Decide Our AI Dev Tool Stack

TL;DR: We now score every AI dev tool candidate partly on how painless a later migration will be — and it changed which tools we adopt now.

The Context

Two months ago, we migrated an internal review pipeline between tools and lost days of embedded behavior: formatting assumptions, rate-limit logic, and fallback branches all had to be rebuilt by hand. No clean handoff existed.

What We Tested

Tool / Approach	Use Case	Verdict	Why
Current stack with documented handoffs	Internal agent pipelines	✅	We can change cost tiers without rebuilding
Lock-in-heavy tool without export paths	Same pipeline	❌	Migration needed full context rewrite

The Pivot Point

A morning incident became an all-day rewrite when we discovered the previous vendor didn’t expose task-state or prompt templates. That single blind spot cost more than the tool saved.

What We Use Now

We run a migration-readiness check before adding any new vendor and maintain a migration manifest for every automation: dependencies, contexts, failure modes, and expectations.

When You’d Choose Differently

If your deployment is standardized and unlikely to change for three years, lock-in is a minor cost. We operate on shorter horizons.

Tool Crucible Rating

7 / 10 — Overall 7 / 10 — Ease 7 / 10 — Value 7 / 10 — Support

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This is part of our AI dev tools evaluation series. See full comparison: [link]