"""
Offline eval for the ExceptionAgent stall decision (core.llm.decide_stall_response).

Scores whether the chosen action is *defensible* (in the case's ``acceptable``
set) and whether it matches the single ``ideal`` action. Stall handling is a
judgment task with more than one right answer, so acceptable-rate is the
headline metric. See evals/_harness.py for scoring details.

Usage:
    export ANTHROPIC_API_KEY=...
    python -m evals.stall_eval                      # built-in cases, 1 run each
    python -m evals.stall_eval --runs 3             # sample each case 3x
    python -m evals.stall_eval --model claude-haiku-4-5
    python -m evals.stall_eval --dry-run            # print contexts, no API calls
    python -m evals.stall_eval --min-pass-rate 0.9  # CI gate

Add real production stall cases to stall_cases.jsonl as they occur — that is
what makes the number trustworthy before flipping EXCEPTION_AGENT_AUTONOMOUS.
"""
from pathlib import Path

import core.llm as llm
from core.llm import build_stall_context, decide_stall_response
from evals._harness import case_now, run_cli

CASES_PATH = Path(__file__).with_name("stall_cases.jsonl")


def to_context(case):
    return build_stall_context(case["minutes_stalled"], now=case_now(case), facts=case.get("facts"))


if __name__ == "__main__":
    run_cli(CASES_PATH, to_context, decide_stall_response, llm)