We Pointed AI at Ten Years of Code and Asked It to Understand. It Couldn't.
Blog post from Luciq
A mobile SDK team describes how early attempts to use AI for research, planning, and implementation on a decade-old iOS and Android codebase produced plausible but conventionally incorrect code that increased review and correction work, leading them to conclude that AI was poorly suited to legacy refactoring without explicit intent and accessible historical context. Rewriting the SDK became necessary both to consolidate fragmented observability data into a unified, OpenTelemetry-aligned platform designed for AI-assisted debugging and to reduce accumulated architectural coupling, duplicated logic, and inconsistent behavior across products. Rather than refactor in place, the team shifted to a greenfield, spec-first approach in which engineers owned design, specifications, contracts, and verification while agents generated scaffolding and implementation, a model validated during a 2026 hack week. They built a graph-based context engine containing incident history, support cases, decision records, and behavioral patterns, and introduced staged workflows with versioned contracts, early integration skeletons, small reviewable phases, automated and human review, mutation testing, chaos testing using historical bugs, and continuous developer feedback. The account emphasizes that AI shifts rather than eliminates bottlenecks—from coding toward decision-making, integration, and verification—and reports that the new kernel is running alongside the legacy SDK in production, with 16 of roughly 29 modules completed and reductions in threads, code size, binary size, and startup time.
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