Anchor: Preventing Artifact Drift for Reliable Agent Verification

Abstract

Reliable evaluation of long-horizon agents requires verifiers that match what the task actually asks. Verifiable rewards have driven progress in domains like math and coding, yet building similarly reliable environments for open-ended enterprise workflows repeatedly struggles to balance realism, verifiability, and scale. This creates a validity problem we call artifact drift: when instructions, environments, oracles, and verifiers are created separately, they frequently fail to agree on what a task requires, resulting in environments that are unsolvable, reward-hackable, or inconsistent. We introduce Anchor, a benchmark-construction pipeline that formalizes domain expert specifications of business workflows into constrained optimization programs. From a single parametric specification, the pipeline jointly produces a natural-language instruction, environment configuration, solver-certified ground-truth solution, and state-based verifier. With this pipeline, altering parameters yields new tasks with controlled difficulty and known optimal solutions, producing harness-agnostic environments whose rewards depend solely on end-state business correctness. We apply Anchor to produce ERP-Bench: a benchmark of 300 long-horizon tasks spanning procurement and manufacturing workflows in a production-grade ERP system. Our pipeline organizes tasks into diverse families with calibrated difficulty profiles, and allows generated environments to evaluate coding, browser, and computer-use agents using a shared verifier. Across these harnesses, we find that frontier models follow all rules and instructions in 25.9% of trials and reach fully optimal decisions in only 17.4% of trials. Overall, Anchor and ERP-Bench offer a concrete recipe for building auditable evaluation environments whose verifiers remain aligned with the economically valuable tasks they are intended to score.

arXiv commentAccepted to the NeurIPS 2026 Workshop on Who Verifies the Agents?