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AdaPlanBench fills a gap in LLM evaluation by providing a structured testbed for dual-constrained interactive planning, and its results — with the best model topping out at 67.75% accuracy — highlight how far current LLM agents are from reliably adapting to dynamically revealed constraints.
The post illustrates how layering a custom prompting skill, project-specific rules, and a dedicated review step addresses the common failure mode of Composer coding too quickly without validating whether the approach fits the project.
The post identifies a concrete workflow — using Plan Mode on an empty project combined with explicit non-goals stored in `CLAUDE.md` — that addresses the common problem of AI agents silently making structural decisions the developer never intended.