$\chi$-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?

End-to-end automation of realistic healthcare operations stresses three capabilities underrepresented in current benchmarks: *policy density*, decisions must be grounded in a large library of medical, insurance, and operational rules; Multi-role *composition*: a single task requires the agent to play multiple roles with handoffs; and *multilateral interaction*: intermediate workflow steps are multi-turn dialogs, such as peer-to-peer review and patient outreach. We introduce $\chi$-Bench, a benchmark of long-horizon healthcare workflows across three domains: provider prior authorization, payer utilization management, and care management. Each task hands the agent a clinical case in a high-fidelity simulator of 20 healthcare apps exposed via 87 MCP tools, which it must drive to a terminal status through tool calls and writing the role's artifacts, guided by a 1,290+ document *managed-care operations handbook* skill. Across 30 agent harness/models configurations, the best agent resolves only **28.0%** of tasks, no agent clears **20%** on strict pass^3, and executing all tasks in a single session slumps the performance to **3.8%**. These results raise the hypothesis that similar gaps are likely to surface in other policy-dense, role-composed, irreversible enterprise domains.