diff --git a/tests/tools/test_audio_mixer_segmented_music.py b/tests/tools/test_audio_mixer_segmented_music.py
new file mode 100644
index 00000000..ebbf51ce
--- /dev/null
+++ b/tests/tools/test_audio_mixer_segmented_music.py
@@ -0,0 +1,119 @@
+"""Regression tests: segmented_music must not attenuate narration.
+
+`_segmented_music` mixed the video's audio with the shaped music via
+`amix=inputs=2`, whose default `normalize=1` divides every input by the input
+count (x0.5 / -6 dB). Unlike `_mix` / `_full_mix`, this path has no `loudnorm`
+stage afterward, so the narration was permanently attenuated across the whole
+timeline — including stretches where the music volume expression is 0. The fix
+adds `normalize=0` (music is already scaled by the `volume` expression, so
+speech must pass at unity).
+"""
+
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from tools.audio.audio_mixer import AudioMixer  # noqa: E402
+
+
+def test_segmented_music_amix_disables_normalize(tmp_path, monkeypatch):
+    """The generated amix must carry normalize=0 (offline, no ffmpeg)."""
+    video = tmp_path / "v.mp4"
+    music = tmp_path / "m.wav"
+    video.write_bytes(b"stub")
+    music.write_bytes(b"stub")
+
+    captured = []
+
+    def fake_run(self, cmd, **kwargs):
+        captured.append(list(cmd))
+
+        class _R:
+            stdout = "10.0\n"
+            stderr = ""
+
+        return _R()
+
+    monkeypatch.setattr(AudioMixer, "run_command", fake_run)
+
+    AudioMixer().execute(
+        {
+            "operation": "segmented_music",
+            "video_path": str(video),
+            "music_path": str(music),
+            "music_volume": 0.2,
+            "segments": [{"start": 1.0, "end": 2.0}],
+            "output_path": str(tmp_path / "out.mp4"),
+        }
+    )
+
+    ffmpeg_cmds = [c for c in captured if c and c[0] == "ffmpeg"]
+    assert ffmpeg_cmds, "no ffmpeg command was built"
+    fc = ffmpeg_cmds[0][ffmpeg_cmds[0].index("-filter_complex") + 1]
+    assert "amix=inputs=2" in fc
+    assert "normalize=0" in fc, f"amix must disable normalize; got: {fc}"
+
+
+@pytest.mark.skipif(shutil.which("ffmpeg") is None, reason="ffmpeg required")
+def test_segmented_music_preserves_narration_level(tmp_path):
+    """End-to-end: narration in a no-music region is not ~6 dB quieter."""
+
+    def _mean_db(path, ss, t):
+        out = subprocess.run(
+            ["ffmpeg", "-ss", str(ss), "-t", str(t), "-i", str(path),
+             "-vn", "-af", "volumedetect", "-f", "null", "-"],
+            capture_output=True, text=True, timeout=60,
+        )
+        for line in out.stderr.splitlines():
+            if "mean_volume" in line:
+                return float(line.split("mean_volume:")[1].strip().split(" ")[0])
+        raise AssertionError("no mean_volume in ffmpeg output")
+
+    video = tmp_path / "vspeech.mp4"
+    music = tmp_path / "mus.wav"
+    subprocess.run(
+        ["ffmpeg", "-y", "-f", "lavfi", "-i", "color=c=black:s=320x240:d=5",
+         "-f", "lavfi", "-i", "sine=frequency=300:duration=5",
+         "-c:v", "libx264", "-c:a", "aac", "-shortest", str(video)],
+        capture_output=True, check=True, timeout=60,
+    )
+    subprocess.run(
+        ["ffmpeg", "-y", "-f", "lavfi", "-i", "sine=frequency=800:duration=3", str(music)],
+        capture_output=True, check=True, timeout=60,
+    )
+
+    # Baseline: the same stereo/aac conversion the tool applies, without any mix.
+    baseline = tmp_path / "base.mp4"
+    subprocess.run(
+        ["ffmpeg", "-y", "-i", str(video),
+         "-af", "aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo",
+         "-c:a", "aac", "-b:a", "192k", str(baseline)],
+        capture_output=True, check=True, timeout=60,
+    )
+
+    out = tmp_path / "out.mp4"
+    result = AudioMixer().execute(
+        {
+            "operation": "segmented_music",
+            "video_path": str(video),
+            "music_path": str(music),
+            "music_volume": 0.2,
+            "segments": [{"start": 1.0, "end": 2.0}],  # music only during [1,2]
+            "output_path": str(out),
+        }
+    )
+    assert result.success, result.error
+
+    baseline_db = _mean_db(baseline, 3, 1)   # no-music region baseline
+    out_db = _mean_db(out, 3, 1)             # no-music region through the tool
+
+    # Narration must track the conversion baseline, not sit ~6 dB below it.
+    assert out_db > baseline_db - 2.0, (
+        f"narration attenuated: baseline {baseline_db} dB, output {out_db} dB"
+    )
diff --git a/tests/tools/test_openai_image_multi_output.py b/tests/tools/test_openai_image_multi_output.py
new file mode 100644
index 00000000..25f6093c
--- /dev/null
+++ b/tests/tools/test_openai_image_multi_output.py
@@ -0,0 +1,91 @@
+"""Regression tests: openai_image must return every image it requests and bills for.
+
+`execute()` requested `n` images from the API and `estimate_cost` scales with
+`n`, but result handling was hardcoded to `response.data[0]` — images 1..n-1
+were decoded never, written never, and absent from `artifacts`. The user paid
+for `n` images and received one. The sibling tools (`grok_image`,
+`dashscope_image`) already loop over every returned image.
+"""
+
+import base64
+import sys
+import types
+from pathlib import Path
+
+import pytest
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+
+class _FakeImage:
+    def __init__(self, payload: bytes):
+        self.b64_json = base64.b64encode(payload).decode()
+
+
+class _FakeResponse:
+    def __init__(self, n: int):
+        self.data = [_FakeImage(f"IMAGE_{i}".encode()) for i in range(n)]
+
+
+class _FakeImages:
+    def generate(self, **kwargs):
+        return _FakeResponse(kwargs["n"])
+
+
+class _FakeClient:
+    def __init__(self, *a, **k):
+        self.images = _FakeImages()
+
+
+@pytest.fixture
+def openai_tool(monkeypatch):
+    # Stub the `openai` SDK so execute() runs fully offline.
+    fake = types.ModuleType("openai")
+    fake.OpenAI = _FakeClient
+    monkeypatch.setitem(sys.modules, "openai", fake)
+    monkeypatch.setenv("OPENAI_API_KEY", "test-key")
+    from tools.graphics.openai_image import OpenAIImage
+
+    return OpenAIImage()
+
+
+def test_all_requested_images_are_written(openai_tool, tmp_path):
+    out = tmp_path / "gen.png"
+    result = openai_tool.execute({"prompt": "p", "n": 4, "output_path": str(out)})
+
+    assert result.success
+    assert result.data["images_generated"] == 4
+    assert len(result.artifacts) == 4
+
+    files = sorted(tmp_path.glob("*.png"))
+    assert len(files) == 4  # every image reached disk, none overwritten
+    contents = {f.read_bytes() for f in files}
+    assert contents == {b"IMAGE_0", b"IMAGE_1", b"IMAGE_2", b"IMAGE_3"}
+
+
+def test_artifacts_match_billed_image_count(openai_tool, tmp_path):
+    # What the user pays for must equal what they receive.
+    inputs = {"prompt": "p", "n": 3, "quality": "high", "output_path": str(tmp_path / "img.png")}
+    result = openai_tool.execute(inputs)
+    billed = openai_tool.estimate_cost(inputs)
+
+    assert len(result.artifacts) == 3
+    assert billed == pytest.approx(0.211 * 3)
+
+
+def test_single_image_keeps_exact_output_path(openai_tool, tmp_path):
+    out = tmp_path / "single.png"
+    result = openai_tool.execute({"prompt": "p", "n": 1, "output_path": str(out)})
+
+    assert result.success
+    assert result.artifacts == [str(out)]
+    assert out.read_bytes() == b"IMAGE_0"
+
+
+def test_multi_output_paths_are_suffixed_and_unique():
+    from tools.graphics.openai_image import OpenAIImage
+
+    paths = OpenAIImage._output_paths("/tmp/art/pic.png", 3, "png")
+    assert [p.name for p in paths] == ["pic_1.png", "pic_2.png", "pic_3.png"]
+    assert len(set(paths)) == 3
diff --git a/tools/audio/audio_mixer.py b/tools/audio/audio_mixer.py
index 04a052e3..dd58d4a5 100644
--- a/tools/audio/audio_mixer.py
+++ b/tools/audio/audio_mixer.py
@@ -653,7 +653,13 @@ class AudioMixer(BaseTool):
             f"volume='{vol_expr}':eval=frame[music_shaped];"
             f"[0:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo[speech];"
             f"[music_shaped]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo[music_fmt];"
-            f"[speech][music_fmt]amix=inputs=2:duration=first:dropout_transition=2[aout]"
+            # normalize=0: amix's default normalize=1 divides every input by the
+            # input count (here x0.5 / -6 dB), which would permanently attenuate
+            # the narration across the whole timeline — including stretches where
+            # the music volume expression is 0. The music is already scaled by the
+            # `volume` expression, so speech must pass at unity. Unlike _mix/
+            # _full_mix, this path has no loudnorm stage to mask the halving.
+            f"[speech][music_fmt]amix=inputs=2:duration=first:dropout_transition=2:normalize=0[aout]"
         )
 
         cmd = [
diff --git a/tools/graphics/openai_image.py b/tools/graphics/openai_image.py
index cabf2c1e..053c5ae5 100644
--- a/tools/graphics/openai_image.py
+++ b/tools/graphics/openai_image.py
@@ -91,6 +91,25 @@ class OpenAIImage(BaseTool):
     side_effects = ["writes image file to output_path", "calls OpenAI API"]
     user_visible_verification = ["Inspect generated image for relevance and quality"]
 
+    @staticmethod
+    def _output_paths(output_path: str | None, count: int, extension: str) -> list[Path]:
+        """Derive one output path per generated image.
+
+        With a single image, honor the requested path as-is. With several,
+        suffix each with `_1`, `_2`, … so no image overwrites another.
+        """
+        ext = extension if extension.startswith(".") else f".{extension}"
+        if not output_path:
+            return [Path(f"generated_image_{idx + 1}{ext}") for idx in range(count)]
+
+        path = Path(output_path)
+        suffix = path.suffix or ext
+        if count == 1:
+            return [path if path.suffix else path.with_suffix(suffix)]
+
+        base = path.with_suffix("") if path.suffix else path
+        return [base.parent / f"{base.name}_{idx + 1}{suffix}" for idx in range(count)]
+
     def get_status(self) -> ToolStatus:
         if os.environ.get("OPENAI_API_KEY"):
             return ToolStatus.AVAILABLE
@@ -132,11 +151,17 @@ class OpenAIImage(BaseTool):
                 n=n,
             )
 
-            image_data = base64.b64decode(response.data[0].b64_json)
-            ext = inputs.get("output_format", "png")
-            output_path = Path(inputs.get("output_path", f"generated_image.{ext}"))
-            output_path.parent.mkdir(parents=True, exist_ok=True)
-            output_path.write_bytes(image_data)
+            items = response.data or []
+            if not items:
+                return ToolResult(success=False, error="OpenAI returned no image outputs")
+
+            ext = output_format
+            output_paths = self._output_paths(inputs.get("output_path"), len(items), ext)
+            outputs: list[str] = []
+            for item, out_path in zip(items, output_paths):
+                out_path.parent.mkdir(parents=True, exist_ok=True)
+                out_path.write_bytes(base64.b64decode(item.b64_json))
+                outputs.append(str(out_path))
 
         except Exception as e:
             return ToolResult(success=False, error=f"OpenAI image generation failed: {e}")
@@ -147,9 +172,11 @@ class OpenAIImage(BaseTool):
                 "provider": "openai",
                 "model": model,
                 "prompt": prompt,
-                "output": str(output_path),
+                "output": outputs[0],
+                "outputs": outputs,
+                "images_generated": len(outputs),
             },
-            artifacts=[str(output_path)],
+            artifacts=outputs,
             cost_usd=self.estimate_cost(inputs),
             duration_seconds=round(time.time() - start, 2),
             model=model,