EvidenceCard: Plan Evidence for Token-Efficient Coding Agents

Tool-using coding agents can consume substantial input tokens while searching repositories, reading source files, and executing tests across long interaction trajectories. Existing token compression methods primarily reduce the size of acquired context or optimize individual context decisions. They do not explicitly track whether the observations collected across a trajectory have satisfied the requirements of the current coding plan. We formulate this limitation as a plan-level evidence management problem and introduce EvidenceCard, a lightweight framework that controls context acquisition through plan-conditioned evidence accounting. EvidenceCard exposes acquisition history and heuristic feedback, prompting the coding agent to reassess whether further context is needed. Its prompt architecture combines a stable, cache-friendly evidence-policy prefix with a small dynamic runtime tail. These signals guide the original agent to acquire, reuse, compress, or stop collecting evidence without additional training and auxiliary LLM calls. In contrast to retrospective compression, EvidenceCard acts prospectively by making evidence sufficiency visible before the next tool action. Empirical results show that EvidenceCard substantially reduces file-read tokens and requests, revealing reductions of up to an order-of-magnitude in read tokens relative to baselines, while maintaining robust task-solving performance.