Don’t Blame Your Agent (Yet): Environment-Aware Error Recovery for Long-Horizon Agents

Long-horizon agents show promise on complex tasks that require many interdependent steps, yet reliable execution remains an open challenge. Existing approaches improve agent components through training, context management, and harness design. We study the complementary problem of online error recovery, which redirects an agent away from a suboptimal intermediate state before it produces a wrong task outcome. Online recovery is particularly important for long-horizon tasks because one error can propagate through dependent later steps. Yet recovery is difficult because an agent's expected behavior depends on environmental preconditions that it can only partially observe. A failed attempt can therefore reflect either inadequate execution or an inaccurate premise about the environment. We propose ALTER, an environment-aware strategy selection framework for online agent error recovery. ALTER separates environmental premises from the behavior expected under them, allowing it to calibrate the actor or revise a premise. A lightweight per-query DSL organizes alternative and combinable strategies, allowing ALTER to select a new strategy under the revised premise. We evaluate ALTER on three long-horizon benchmarks with three actor models, comparing it with the original agent without recovery and five error or failure recovery baselines. ALTER improves task success by 5.9–47.1 % over the original agent and by 7.1–41.2 % over the strongest environment-unaware recovery baseline, while achieving broader coverage of resolution strategies.