How to test whether an AI agent escalates conflicting evidence

Test what the agent does with disagreement, not just whether its final answer happens to be right. Pair conflicting-evidence cases with consistent-evidence controls; preserve the visible trace; score recognition, bounded resolution, and disclosure of residual uncertainty separately from answer accuracy and needless hedging. A polished answer can conceal an unresolved source conflict, while a cautious answer can still fail the task.

Define the conflict before running the agent

Start with an answerable question and two evidence conditions. In the conflict condition, retrieved material contradicts another source or a previously established answer. In the control condition, keep the question and evidence count the same, but make the sources consistent. Record source versions, timestamps, the authority rule, and the expected resolution in the evaluator's key.

The paper uses matched question-and-passage-count controls for its controlled setting. Its naturally occurring multi-step conflict and control sets use different questions, so they do not support the same paired causal comparison. An older and a newer policy page with different limits is one illustrative test case, not a case from the paper. If authority cannot be determined, the expected behavior is to expose the unresolved disagreement, not invent a winner.

Grade three behaviors, then the outcome

Sun and colleagues call their trajectory-level dimensions Identify, Solve, Escalate. They measure different failures; do not collapse them into a single humility score. The table below is an operational adaptation, not a validated scoring threshold.

The paper's Solve measure counts a post-recognition, nonduplicate tool call and clean termination—an attempt, not proof that the answer became correct. Its Escalate measure checks acknowledgment in incorrect final answers on conflict cases. For a local release test, adjudicate actual resolution separately. A lucky correct answer does not prove the agent understood the discrepancy.

Keep the request, source versions, visible observations, tool calls, final response, and rubric decision in one eval record. OpenAI's agent-evaluation guidance recommends full traces for diagnosing model calls, tool calls, guardrails, and handoffs, with structured graders and repeatable datasets for regressions. If private reasoning is unavailable, grade observable events; do not infer an internal recognition step from fluent prose.

Run conflict and control cases together

A conflict suite should also penalize unnecessary caution. When sources agree and support an answer, the agent should answer rather than reflexively escalate. Compare a candidate policy with its prior version on both conditions. Report answer accuracy, conflict recognition, relevant follow-up, unresolved-conflict disclosure, and over-escalation on controls. Keep severity and source authority explicit in the local acceptance decision; see Bonfire's agent release-threshold process.

The paper reports that higher accuracy did not reliably imply stronger conflict handling across its evaluated configurations. A one-clause prompt increased escalation in several settings while often reducing accuracy. Those findings justify measuring both outcomes; they do not establish that a generic “be humble” instruction improves production reliability. The study tested five fixed harness–backbone configurations, did not isolate those components, and did not establish prompted-versus-default significance with the reported conflict/control test.

Put the result in the right release gate

This test asks whether evidence disagreement survives the workflow into the final response. It complements stateful-outcome evaluation, which checks what actually changed, and false-success tests after tool failure, which check unsupported completion claims. Do not substitute one for another.

Before using a score to release an agent, review a sample of grader decisions against the traces and source key, especially cases where the agent may have resolved the conflict despite an unusual answer. Then set a local pass rule protecting both answer quality and honest escalation. The paper's dimensions are diagnostic, not a universal production threshold.

Limits of the evidence

The arXiv v1 study evaluates constructed and selected knowledge-conflict settings, not arbitrary ambiguous requests, missing capabilities, or live enterprise deployment. Naturally occurring controls differ in question composition from conflict cases. Judge labels and access to reasoning traces are method-dependent. Cross-judge and limited human checks do not prove a general ranking of agents or a universal prompt fix.

Bottom line: pair conflict cases with controls, record the visible evidence trail, and grade conflict handling independently from task success. Escalate only what remains unresolved, and verify that better disclosure does not quietly erode answer quality.