HERA: Harness–Environment Co-Evolution for Reliable Agentic Abstention

Large language model (LLM) agents are increasingly capable of acting in com- plex tool-use environments, yet they often fail to recognize when tasks are in- feasible and no valid solution exists. Recent work has formalized this reliabil- ity gap as the problem of agentic abstention, and existing approaches typically optimize a model or agent harness against a fixed set of tasks, leading to lim- ited generalization to unseen failure modes. We introduce HERA, a framework for harness–environment co-evolution for agentic abstention. HERA consists of (i) a pipeline to automatically construct verifiable pairs of feasible and infeasible tasks by applying controlled environment mutations that transform solvable tasks into cases requiring abstention, and (ii) a co-evolution procedure in which perfor- mance failures on previous tasks are used to drive harness adaptation and generate new execution environments and tasks geared towards previous weaknesses. On held-out evaluation tasks, an evolved harness from HERA improves abstention accuracy from 61.7% to 83.3% while maintaining feasible task performance, out- performing all compared methods. The resulting best harness transfers across 19 other LLMs, improving abstention accuracy by 15.3 percentage points on average without any model-specific optimization, and enabling smaller models to match the performance of more powerful models at an estimated 85% lower cost.