Assurance Envelopes for Autonomous Coding Agents: Minimum-Cost Evidence for Software Change

• URL: http://arxiv.org/abs/2609.16302v1
• Date: Mon, 14 Sep 2026 20:06:46 GMT
• Title: Assurance Envelopes for Autonomous Coding Agents: Minimum-Cost Evidence for Software Change
• Abstract summary: We ask which least-cost subset of available evidence re-establishes a required property.<n>We validate every selection by that closure rather than by trusting the closure.<n>Small graphs from Rust, IronBlocks, and Pong outcomes show that the minimum envelope depends on the task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When a coding agent returns to existing software, it inherits evidence from earlier engineering work: tests, type checks, proofs, static analyses, and traces. Reloading all of it is wasteful, but dropping a piece the change depends on can leave a required property unsupported. Given the properties a change must preserve, its obligations, we ask which least-cost subset of the available evidence re-establishes them, and we call such a subset a task-conditioned assurance envelope. Evidence and the rules that combine it form a typed inference graph; an obligation is met when forward chaining from the selected evidence reaches it, and we validate every selection by that closure rather than by trusting the optimizer. The software-derived graphs in our evaluation come from preserved outcomes of prior AI coding-agent runs; we freeze those artifacts and ask which accumulated evidence should be restored for a later task. Small graphs from Rust, IronBlocks, and Pong outcomes show that the minimum envelope depends on the task, that none may exist when current evidence cannot re-establish a required property, that some properties need several pieces of evidence together, and that expanding the requirements adds evidence rather than replacing it. A prespecified synthetic benchmark of 249 instances characterizes computation: a baseline that discards the 'several pieces together' structure necessarily fails to re-derive them; every completed exact cross-check agreed with the CP-SAT optimizer; and median solve time stayed below 20 ms at 500-evidence graphs, except that graphs with many alternative derivations per target timed out at far smaller sizes, so structure, not raw size, drives difficulty. The contribution is a bounded application of established optimization to selecting assurance context for a software change; discovering the obligations and downstream agent benefit remain open.

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