diff --git a/scripts/ci_report.py b/scripts/ci_report.py
new file mode 100644
index 0000000..85383a4
--- /dev/null
+++ b/scripts/ci_report.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""Report aggregate pass rates with 95% Wilson CIs and paired McNemar.
+
+Accepts a per-instance results JSON in one of two shapes:
+
+* a mapping ``{task_id: true/false}``
+* a list of records ``[{"id": ..., "success": true/false}, ...]``
+
+and prints the aggregate pass rate with a 95% Wilson score interval. With
+``--compare``, runs an exact paired McNemar test between two agents over
+their shared task set.
+
+The input file is a convenience interface: it must be derived from
+AgentBench run outputs. This script does not parse native artifacts such
+as ``overall.json`` (task-level aggregate metrics, format varies per task)
+or the raw ``db_out_new.jsonl``/``dev.jsonl`` trajectory logs.
+
+Example::
+
+    python scripts/ci_report.py --results agent_a.json
+    python scripts/ci_report.py --results agent_a.json --compare agent_b.json
+"""
+
+import argparse
+import json
+import math
+from collections import OrderedDict
+
+
+def wilson_ci(successes: int, n: int, z: float = 1.96):
+    """(lower, upper) Wilson score interval for a proportion."""
+    if n <= 0:
+        return (0.0, 0.0)
+    p = successes / n
+    denom = 1 + z * z / n
+    center = (p + z * z / (2 * n)) / denom
+    margin = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / denom
+    return (max(0.0, center - margin), min(1.0, center + margin))
+
+
+def paired_mcnemar(a_only: int, b_only: int, two_sided: bool = True) -> float:
+    """Exact binomial p-value for the paired McNemar test."""
+    n = a_only + b_only
+    if n == 0:
+        return 1.0
+    k = min(a_only, b_only)
+    tail = 0.0
+    for i in range(k + 1):
+        tail += math.comb(n, i) / (2 ** n)
+    return min(1.0, 2 * tail) if two_sided else tail
+
+
+def load_instances(path):
+    with open(path, encoding="utf-8") as f:
+        data = json.load(f)
+    if isinstance(data, dict):
+        return OrderedDict((str(k), bool(v)) for k, v in data.items())
+    return OrderedDict((str(r.get("id", i)), bool(r["success"])) for i, r in enumerate(data))
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--results", required=True, help="per-instance results JSON")
+    parser.add_argument("--compare", help="second results JSON for McNemar")
+    args = parser.parse_args()
+
+    per_task = load_instances(args.results)
+    n = len(per_task)
+    if n == 0:
+        raise SystemExit("no instances found")
+    successes = sum(per_task.values())
+    lo, hi = wilson_ci(successes, n)
+    print(f"tasks: {n}")
+    print(f"pass rate: {successes / n:.4f}  95% CI [{lo:.4f}, {hi:.4f}]")
+
+    if args.compare:
+        other = load_instances(args.compare)
+        shared = [t for t in per_task if t in other]
+        if not shared:
+            raise SystemExit("no shared tasks between the two result files")
+        a_only = sum(1 for t in shared if per_task[t] and not other[t])
+        b_only = sum(1 for t in shared if other[t] and not per_task[t])
+        both = sum(1 for t in shared if per_task[t] and other[t])
+        p = paired_mcnemar(a_only, b_only)
+        print(f"paired comparison on {len(shared)} shared tasks")
+        print(f"both: {both}  | A-only: {a_only}  | B-only: {b_only}")
+        print(f"McNemar two-sided p: {p:.4f}")
+
+
+if __name__ == "__main__":
+    main()