FlowPlan: Reducing the Token Cost of Coding Agents with Abstraction-Guided Planning and Verification

Melika Sepidband, Jiho Shin, Song Wang, Hadi Hemmati

LLM-based coding agents such as Claude Code can resolve real-world repository issues, but their iterative interaction loop can be expensive. Existing approaches reduce this cost primarily by limiting, compressing, or selectively avoiding information and actions within the repair process. We investigate a complementary question: Can an agent reason about a repair at an abstract program level, rather than repeatedly operating over raw source code, to reduce both the exploration needed to identify a repair and the repeated checking needed to verify it afterward? We present FlowPlan, an abstraction-guided repair approach that structures localization, planning, and verification around per-function control-flow or data-flow graphs. The agent first localizes the candidate fault location on the program abstraction and expresses the intended repair as explicit graph deltas before modifying the concrete source. After the edit, FlowPlan reconstructs the same abstraction and compares the observed structural change with the planned one. Then, a verification checks the complete repair before termination. Across all six benchmark-model settings, FlowPlan does not significantly change resolution…