Coding agents working on sequential maintenance tasks re-read the same files across related tasks, wasting more than a third of their input tokens on redundant context. The natural fix, caching file summaries, fails to reliably reduce cost: tool-use traces are consistent with agents over-relying on generic descriptions, skipping re-reads, and offsetting token savings with less effective downstream decisions, leaving net cost roughly unchanged. We introduce Prism, a content-hash-keyed file memory that augments summaries with bounded facets: identifier-grounded facts distilled from the agent’s own prior reasoning. On 12 SWE-Chain dependency-upgrade chains, Prism reduces cost by 24.9% on GPT-5 and 9.9% on GPT-5.4-mini, while maintaining comparable aggregate Build+Fix F1. Against no memory, a summary-only ablation yields only a nominal 2.4% cost decrease that falls within run-to-run variability, while a static Aider-style repo-map baseline costs substantially more at 13% over no memory. Two controlled ablations isolate the causal contributions: wiping the persistent store before every task raises cost by 37% over no memory, and replacing facets with a 3× larger unstructured summary raises it by 41%. Code: https://github.com/WhiteFlamesCN/Prism-Opencode.

Graph databases are increasingly queried through natural language, yet every existing benchmark evaluates isolated single-turn queries rather than the multi-turn sessions through which analysts actually work. We introduce CypherTurn, the first benchmark for conversational Text-to-Cypher evaluation, comprising 721 sessions and 5,927 turns across 7 knowledge graphs and 13 conversational phenomena. We evaluate 15 models under a guided oracle protocol and a fully autonomous agentic protocol, yielding four findings. First, the best model reaches only 64.7% execution accuracy, and session-level correctness remains below 5%. Second, despite strong overall rank correlation, frontier models exhibit a consequential reordering of the top of the leaderboard under autonomous operation, a phenomenon we term the Autonomy Divergence, which reveals error-management as a partially independent capability from raw generation skill. Third, scaling action budgets from ×3 to ×10 fails to close the autonomy gap, as frontier models self-limit to approximately two actions per turn regardless of available budget. Fourth, single-turn Cypher fine-tuning degrades multi-turn instruction following, while architecture-appropriate specialization outperforms several frontier models. These results establish CypherTurn as an open challenge for conversational graph database reasoning. Code and data are available at https://github.com/BarryQ/CypherTurn.