diff --git a/src/agents/createFaceDetectionAgent.ts b/src/agents/createFaceDetectionAgent.ts
new file mode 100644
index 0000000..9bad54f
--- /dev/null
+++ b/src/agents/createFaceDetectionAgent.ts
@@ -0,0 +1,35 @@
+import { ToolLoopAgent, Output, stepCountIs } from "ai";
+import { z } from "zod";
+
+const faceDetectionSchema = z.object({
+  hasFace: z.boolean(),
+});
+
+const instructions = `You classify images as face guides or not.
+
+A face guide is a headshot or portrait photo on a plain or white background, used for face-swapping in AI image generation. It shows a person's face clearly as the primary subject.
+
+These are NOT face guides:
+- Playlist covers or album art (even if they show a person)
+- Promotional graphics with text overlays
+- Concert photos or action shots
+- Logos or branded images
+- Any image where the face is not the sole focus on a clean background
+
+Return hasFace: true ONLY for face guide images (headshots on plain backgrounds).
+Return hasFace: false for everything else.`;
+
+/**
+ * Creates a ToolLoopAgent configured for face guide detection in images.
+ * Uses Output.object with a Zod schema for structured boolean response.
+ *
+ * @returns A configured ToolLoopAgent using Google Gemini via AI Gateway.
+ */
+export function createFaceDetectionAgent() {
+  return new ToolLoopAgent({
+    model: "google/gemini-3.1-flash-lite-preview",
+    instructions,
+    output: Output.object({ schema: faceDetectionSchema }),
+    stopWhen: stepCountIs(1),
+  });
+}
diff --git a/src/content/__tests__/detectFace.test.ts b/src/content/__tests__/detectFace.test.ts
index 6a8bf5b..0b4c84d 100644
--- a/src/content/__tests__/detectFace.test.ts
+++ b/src/content/__tests__/detectFace.test.ts
@@ -4,9 +4,11 @@ vi.mock("../../sandboxes/logStep", () => ({
   logStep: vi.fn(),
 }));
 
-const mockFalSubscribe = vi.fn();
-vi.mock("../falSubscribe", () => ({
-  falSubscribe: (...args: unknown[]) => mockFalSubscribe(...args),
+const mockGenerate = vi.fn();
+vi.mock("../../agents/createFaceDetectionAgent", () => ({
+  createFaceDetectionAgent: () => ({
+    generate: mockGenerate,
+  }),
 }));
 
 import { detectFace } from "../detectFace";
@@ -16,96 +18,56 @@ describe("detectFace", () => {
     vi.clearAllMocks();
   });
 
-  it("returns true when a person label is detected", async () => {
-    mockFalSubscribe.mockResolvedValue({
-      data: {
-        results: {
-          bboxes: [[10, 20, 100, 200]],
-          labels: ["person"],
-        },
-      },
-    });
+  it("returns true when the agent detects a face guide", async () => {
+    mockGenerate.mockResolvedValue({ output: { hasFace: true } });
 
     const result = await detectFace("https://example.com/headshot.png");
 
     expect(result).toBe(true);
-    expect(mockFalSubscribe).toHaveBeenCalledWith(
-      "fal-ai/florence-2-large/object-detection",
-      { image_url: "https://example.com/headshot.png" },
-    );
   });
 
-  it("returns true when a face label is detected among other objects", async () => {
-    mockFalSubscribe.mockResolvedValue({
-      data: {
-        results: {
-          bboxes: [[0, 0, 50, 50], [10, 20, 100, 200]],
-          labels: ["chair", "human face"],
-        },
-      },
-    });
-
-    const result = await detectFace("https://example.com/photo.png");
-
-    expect(result).toBe(true);
-  });
-
-  it("returns false when no person or face labels are detected", async () => {
-    mockFalSubscribe.mockResolvedValue({
-      data: {
-        results: {
-          bboxes: [[0, 0, 300, 300]],
-          labels: ["album cover"],
-        },
-      },
-    });
+  it("returns false when the agent detects no face guide", async () => {
+    mockGenerate.mockResolvedValue({ output: { hasFace: false } });
 
     const result = await detectFace("https://example.com/album-cover.png");
 
     expect(result).toBe(false);
   });
 
-  it("returns false when results are empty", async () => {
-    mockFalSubscribe.mockResolvedValue({
-      data: {
-        results: {
-          bboxes: [],
-          labels: [],
-        },
-      },
-    });
+  it("sends a few-shot example with the face guide reference image", async () => {
+    mockGenerate.mockResolvedValue({ output: { hasFace: true } });
 
-    const result = await detectFace("https://example.com/blank.png");
+    await detectFace("https://example.com/photo.png");
 
-    expect(result).toBe(false);
-  });
+    const callArgs = mockGenerate.mock.calls[0][0];
+    const messages = callArgs.messages;
 
-  it("returns false when detection fails", async () => {
-    mockFalSubscribe.mockRejectedValue(new Error("Detection failed"));
+    // First message: example face guide image URL + question
+    expect(messages[0].role).toBe("user");
+    const exampleImagePart = messages[0].content.find((p: { type: string }) => p.type === "image");
+    expect(exampleImagePart).toBeDefined();
+    expect(exampleImagePart.image).toContain("face-guide-example.png");
 
-    const result = await detectFace("https://example.com/broken.png");
+    // Second message: assistant answer for the example
+    expect(messages[1].role).toBe("assistant");
 
-    expect(result).toBe(false);
+    // Third message: actual image to classify
+    expect(messages[2].role).toBe("user");
+    const targetImagePart = messages[2].content.find((p: { type: string }) => p.type === "image");
+    expect(targetImagePart.image).toBe("https://example.com/photo.png");
   });
 
-  it("does not false-positive on labels containing face words as substrings", async () => {
-    mockFalSubscribe.mockResolvedValue({
-      data: {
-        results: {
-          bboxes: [[0, 0, 200, 200]],
-          labels: ["ottoman", "mannequin", "womanizer"],
-        },
-      },
-    });
+  it("returns false when the agent throws", async () => {
+    mockGenerate.mockRejectedValue(new Error("Model error"));
 
-    const result = await detectFace("https://example.com/furniture.png");
+    const result = await detectFace("https://example.com/broken.png");
 
     expect(result).toBe(false);
   });
 
   it("logs the error when detection fails", async () => {
     const { logStep } = await import("../../sandboxes/logStep");
-    mockFalSubscribe.mockRejectedValue(new Error("Rate limit exceeded"));
+    mockGenerate.mockRejectedValue(new Error("Rate limit exceeded"));
 
     await detectFace("https://example.com/broken.png");
 
@@ -115,4 +77,12 @@ describe("detectFace", () => {
       expect.objectContaining({ error: "Rate limit exceeded" }),
     );
   });
+
+  it("returns false when output is null", async () => {
+    mockGenerate.mockResolvedValue({ output: null });
+
+    const result = await detectFace("https://example.com/broken.png");
+
+    expect(result).toBe(false);
+  });
 });
diff --git a/src/content/detectFace.ts b/src/content/detectFace.ts
index 4715686..a37ebb4 100644
--- a/src/content/detectFace.ts
+++ b/src/content/detectFace.ts
@@ -1,34 +1,47 @@
 import { logStep } from "../sandboxes/logStep";
-import { falSubscribe } from "./falSubscribe";
+import { createFaceDetectionAgent } from "../agents/createFaceDetectionAgent";
 
-const DETECTION_MODEL = "fal-ai/florence-2-large/object-detection";
-
-/** Labels that indicate a human face or person is present in the image. */
-const FACE_LABELS = ["person", "face", "human face", "man", "woman", "boy", "girl"];
+const FACE_GUIDE_EXAMPLE_URL =
+  "https://dxfamqbi5zyezrs5.public.blob.vercel-storage.com/content-attachments/image/1775671967694-face-guide-example.png";
 
 /**
- * Detects whether an image contains a human face using Florence-2 object detection.
+ * Detects whether an image is a face guide (headshot/portrait on a plain background)
+ * rather than a playlist cover, album art, or other image that may incidentally contain a face.
+ *
+ * Uses a few-shot approach: shows the model an example face guide first, then asks
+ * it to classify the target image.
  *
  * @param imageUrl - URL of the image to analyze
- * @returns true if at least one face/person is detected, false otherwise
+ * @returns true if the image is a face guide, false otherwise
  */
 export async function detectFace(imageUrl: string): Promise<boolean> {
   try {
-    const result = await falSubscribe(DETECTION_MODEL, {
-      image_url: imageUrl,
+    const agent = createFaceDetectionAgent();
+    const { output } = await agent.generate({
+      messages: [
+        {
+          role: "user",
+          content: [
+            { type: "image", image: FACE_GUIDE_EXAMPLE_URL },
+            { type: "text", text: "This is an example of a face guide — a headshot or portrait on a plain/white background used for face-swapping. Is this a face guide?" },
+          ],
+        },
+        {
+          role: "assistant",
+          content: [{ type: "text", text: '{"hasFace":true}' }],
+        },
+        {
+          role: "user",
+          content: [
+            { type: "image", image: imageUrl },
+            { type: "text", text: "Is this image a face guide like the example above? A face guide is a headshot or portrait on a plain background. Playlist covers, album art, promotional graphics, and other images that happen to show a face are NOT face guides." },
+          ],
+        },
+      ],
     });
 
-    const data = result.data as Record<string, unknown>;
-    const results = data.results as { labels?: string[] } | undefined;
-    const labels = results?.labels ?? [];
-
-    const hasFace = labels.some((label) => {
-      const lower = label.toLowerCase();
-      return FACE_LABELS.some(
-        (faceLabel) => lower === faceLabel || lower.split(" ").includes(faceLabel),
-      );
-    });
-    logStep("Face detection result", false, { imageUrl: imageUrl.slice(0, 80), hasFace, labels });
+    const hasFace = output?.hasFace ?? false;
+    logStep("Face detection result", false, { imageUrl: imageUrl.slice(0, 80), hasFace });
     return hasFace;
   } catch (err) {
     logStep("Face detection failed, assuming no face", false, {