How do you avoid model and vendor lock-in with AI coding agents?

Vendor lock-in in AI agent systems should be assessed layer by layer rather than as a single platform decision, since factory definitions, data, compute, inference, improvement tools, orchestration, and access integrations each have different switching costs. The text recommends retaining ownership or portability for high-risk layers such as code-based agent configurations, data and context, compute, and especially inference choices, while using managed services for more replaceable functions like orchestration and access. Teams can evaluate vendors through questions about model and harness flexibility, data export and retention policies, and whether prompts and routing logic are stored as version-controlled files rather than proprietary interfaces. It cautions against focusing too heavily on easily replaced orchestration tools, equating self-hosting with portability, building every layer internally, or assuming model choices will remain optimal. Warp Factories is presented as an example of an infrastructure-oriented approach that supports code-defined configurations, multiple models and agent harnesses, customer-controlled data and hosting, and the ability to benchmark and reroute workloads without replatforming.

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