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AI-forward engineering teams are seeing more code rewritten or reverted soon after it ships. High agent use pulls in dependencies automatically and generates more code for humans to review, until the remediation work behind it no longer scales at the same rate.
That gap between shipping speed and durability is where maintenance load builds. The teams that build rigor into the pipeline will move faster for longer, while the ones that don’t will spend 2027 paying down debt they didn’t know they were accumulating.
To keep pace, teams need to shift how they work: tracking AI-generated code separately from human-written code, measuring speed and quality together rather than output volume alone, and building enforcement into the pipeline itself.
In this panel, we’ll bring engineering leaders together to unpack the maintenance debt your teams are actually creating, and how to close it before it becomes next quarter’s bottleneck.
Key takeaways:
- To measure the maintenance debt hiding behind your AI velocity numbers
- To track AI-generated code separately, before its debt blends into your baseline
- To build quality gates that catch problems before they reach review, not after
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