When Success Is a Lossy Record: Matched-Success Continuation Evaluation for Persistent Agents

Agent benchmarks reduce a completed episode to a verdict such as PASS, discarding the environment that later tasks inherit. We formalize this compression failure as continuation insufficiency: the excess Bayes risk of predicting future outcomes from the verdict rather than the state under declared continuation conditions. Matched-success evaluation forks equally accepted states into identical frozen continuations and separates transition, preservation, downstream utility, and cost. Across 360 controlled coding pairs, later success differs by 27.50 percentage points; a complementary 240-pair enterprise study shows an 8.75-point shift in the same direction. Among 64 naturally produced successful states, a 2.73-point reference-free separation survives crossed randomization (p = 0.0023). On 144 held-out executions in 18 repositories, retaining continuation information avoids 13.19 points of verdict-only choice regret and recovers the historical-state baseline. Replay and recovery identify history compatibility as the principal omitted variable, while a common-snapshot control isolates downstream divergence in Jsonpickle. Success is therefore a lossy record of an agent's work: our protocol detects the missing information, locates its mechanism, and prices the cost of discarding it.