diff --git a/docs/features/structured-output.mdx b/docs/features/structured-output.mdx
index 0b970564..8ce988e0 100644
--- a/docs/features/structured-output.mdx
+++ b/docs/features/structured-output.mdx
@@ -52,6 +52,7 @@ asyncio.run(main())
 
 **Stage 2: Extraction (Post-Completion)**
 - `StructuredOutputAgent` receives the final answer text
+- Valid JSON answers are validated locally with Pydantic first
 - Uses LLM's `astructured_predict()` to extract data into your model
 - Validates against schema and returns typed object or `None`
 
@@ -152,6 +153,21 @@ agent = MobileAgent(
 )
 ```
 
+### Setting the Schema After Initialization
+
+You can also attach the schema before the run starts:
+
+```python
+agent = MobileAgent(
+    goal="Find John Smith's contact information",
+    config=MobileConfig(),
+)
+
+agent.set_output_schema(ContactInfo)
+result = await agent.run()
+contact = result.structured_output
+```
+
 ### Reasoning Mode
 
 Works in both direct and reasoning modes:
diff --git a/docs/sdk/droid-agent.mdx b/docs/sdk/droid-agent.mdx
index a3c824ae..16d0769d 100644
--- a/docs/sdk/droid-agent.mdx
+++ b/docs/sdk/droid-agent.mdx
@@ -220,6 +220,19 @@ if result.success and result.structured_output:
     print(f"Condition: {weather.condition}")
 ```
 
+You can also configure the schema after initialization, before the workflow starts:
+
+```python
+agent = MobileAgent(
+    goal="Open weather app and get current weather",
+    config=config,
+)
+agent.set_output_schema(WeatherInfo)
+
+result = await agent.run()
+weather = result.structured_output
+```
+
 <a id="mobilerun.agent.droid.droid_agent.MobileAgent.run"></a>
 
 #### MobileAgent.run
diff --git a/mobilerun/agent/droid/droid_agent.py b/mobilerun/agent/droid/droid_agent.py
index 4f04c64c..a7953185 100644
--- a/mobilerun/agent/droid/droid_agent.py
+++ b/mobilerun/agent/droid/droid_agent.py
@@ -356,6 +356,22 @@ def run(self, *args, **kwargs) -> Awaitable[ResultEvent] | WorkflowHandler:
         handler = super().run(*args, **kwargs)  # type: ignore[assignment]
         return handler
 
+    def set_output_schema(self, output_model: Type[BaseModel]) -> "MobileAgent":
+        """Configure the Pydantic model used for structured output extraction."""
+        if not isinstance(output_model, type) or not issubclass(
+            output_model, BaseModel
+        ):
+            raise TypeError("output_model must be a Pydantic BaseModel subclass")
+
+        self.output_model = output_model
+
+        if self.structured_output_llm is None:
+            self.structured_output_llm = self.fast_agent_llm
+        if self.manager_agent is not None:
+            self.manager_agent.output_model = output_model
+
+        return self
+
     # ========================================================================
     # start_handler — creates driver, registry, action_ctx
     # ========================================================================
diff --git a/mobilerun/agent/oneflows/structured_output_agent.py b/mobilerun/agent/oneflows/structured_output_agent.py
index e7d17276..3293ef06 100644
--- a/mobilerun/agent/oneflows/structured_output_agent.py
+++ b/mobilerun/agent/oneflows/structured_output_agent.py
@@ -1,33 +1,83 @@
 """
 StructuredOutputAgent - Extract structured data from final answers.
 
-Takes a raw text answer and a Pydantic model, uses structured_predict()
-to extract structured data from the text.
+Takes a raw text answer and a Pydantic model, then returns a validated model
+instance. Answers that already contain JSON are parsed locally first; otherwise
+the agent falls back to LLM structured extraction.
 """
 
+import json
 import logging
-from typing import Type
+import re
+from collections.abc import Iterator
+from typing import Any, Type, TypeVar
 
 from llama_index.core.llms.llm import LLM
 from llama_index.core.prompts import PromptTemplate
 from llama_index.core.workflow import Context, StartEvent, StopEvent, Workflow, step
-from pydantic import BaseModel
+from pydantic import BaseModel, ValidationError
 
 from mobilerun.agent.utils.inference import astructured_predict_with_retries
 
 logger = logging.getLogger("mobilerun")
 
+T = TypeVar("T", bound=BaseModel)
+
+_FENCED_JSON_RE = re.compile(
+    r"```(?:json)?\s*(.*?)```",
+    re.IGNORECASE | re.DOTALL,
+)
+
+
+def coerce_structured_output_from_text(
+    pydantic_model: Type[T], answer_text: str
+) -> T | None:
+    """Return a validated model when *answer_text* already contains JSON."""
+
+    for candidate in _iter_json_candidates(answer_text):
+        try:
+            if isinstance(candidate, str):
+                return pydantic_model.model_validate_json(candidate)
+            return pydantic_model.model_validate(candidate)
+        except (TypeError, ValueError, ValidationError):
+            continue
+    return None
+
+
+def _iter_json_candidates(text: str) -> Iterator[str | Any]:
+    stripped = text.strip()
+    if not stripped:
+        return
+
+    yield stripped
+
+    for match in _FENCED_JSON_RE.finditer(text):
+        candidate = match.group(1).strip()
+        if candidate:
+            yield candidate
+
+    decoder = json.JSONDecoder()
+    for index, char in enumerate(text):
+        if char not in "{[":
+            continue
+        try:
+            value, _ = decoder.raw_decode(text[index:])
+        except json.JSONDecodeError:
+            continue
+        yield value
+
 
 class StructuredOutputAgent(Workflow):
     """
     Agent that extracts structured output from text answers.
 
-    Uses LLM.structured_predict() to parse text into Pydantic models.
+    Uses direct Pydantic validation for JSON answers, then
+    LLM.structured_predict() for natural-language answers.
     """
 
     def __init__(
         self,
-        llm: LLM,
+        llm: LLM | None,
         pydantic_model: Type[BaseModel],
         answer_text: str,
         **kwargs,
@@ -42,23 +92,41 @@ async def extract_structured_output(
         self, ctx: Context, ev: StartEvent
     ) -> StopEvent:
         """
-        Extract structured output using structured_predict().
+        Extract structured output using direct validation or structured_predict().
         """
-        logger.debug("🔍 Extracting structured output from final answer...")
+        logger.debug("Extracting structured output from final answer...")
 
         try:
-            # Create prompt for extraction
+            direct_output = coerce_structured_output_from_text(
+                self.pydantic_model,
+                self.answer_text,
+            )
+            if direct_output is not None:
+                logger.debug("Parsed structured output directly from final answer")
+                return StopEvent(
+                    result={
+                        "structured_output": direct_output,
+                        "success": True,
+                        "error_message": "",
+                    }
+                )
+
+            if self.llm is None:
+                raise ValueError(
+                    "No structured output LLM is configured and the final answer "
+                    "does not contain valid JSON for the requested model"
+                )
+
             prompt = PromptTemplate(
                 "Extract structured information from the following text:\n\n{text}"
             )
 
-            # Use structured_predict to extract data
-            logger.info("🔍 StructuredOutput response:", extra={"color": "magenta"})
+            logger.info("StructuredOutput response:", extra={"color": "magenta"})
             structured_output = await astructured_predict_with_retries(
                 self.llm, self.pydantic_model, prompt, text=self.answer_text
             )
 
-            logger.debug("✅ Successfully extracted structured output")
+            logger.debug("Successfully extracted structured output")
 
             return StopEvent(
                 result={
@@ -69,7 +137,7 @@ async def extract_structured_output(
             )
 
         except Exception as e:
-            logger.error(f"❌ Failed to extract structured output: {e}")
+            logger.error(f"Failed to extract structured output: {e}")
 
             return StopEvent(
                 result={
diff --git a/tests/test_structured_output.py b/tests/test_structured_output.py
new file mode 100644
index 00000000..d55c322c
--- /dev/null
+++ b/tests/test_structured_output.py
@@ -0,0 +1,112 @@
+import asyncio
+import unittest
+
+from pydantic import BaseModel, Field
+
+from mobilerun import MobileAgent
+from mobilerun.agent.oneflows.structured_output_agent import (
+    StructuredOutputAgent,
+    coerce_structured_output_from_text,
+)
+from mobilerun.config_manager import MobileConfig
+
+
+class ContactInfo(BaseModel):
+    name: str = Field(description="Full name")
+    phone: str
+    email: str | None = None
+
+
+class StructuredOutputCoercionTest(unittest.TestCase):
+    def test_validates_raw_json_answer(self):
+        result = coerce_structured_output_from_text(
+            ContactInfo,
+            '{"name": "Grace Liu", "phone": "+1 555 0100", "email": "grace@example.com"}',
+        )
+
+        self.assertIsInstance(result, ContactInfo)
+        self.assertEqual(result.name, "Grace Liu")
+        self.assertEqual(result.phone, "+1 555 0100")
+
+    def test_validates_fenced_json_answer(self):
+        result = coerce_structured_output_from_text(
+            ContactInfo,
+            """
+Done.
+
+```json
+{"name": "Ada Lovelace", "phone": "+44 20 7946 0958"}
+```
+""",
+        )
+
+        self.assertIsInstance(result, ContactInfo)
+        self.assertEqual(result.name, "Ada Lovelace")
+        self.assertIsNone(result.email)
+
+    def test_ignores_plain_text_without_json_shape(self):
+        result = coerce_structured_output_from_text(
+            ContactInfo,
+            "I found Grace Liu's phone number, but this is not JSON.",
+        )
+
+        self.assertIsNone(result)
+
+    def test_structured_output_agent_accepts_json_without_llm(self):
+        async def run_agent():
+            handler = StructuredOutputAgent(
+                llm=None,
+                pydantic_model=ContactInfo,
+                answer_text='{"name": "Grace Liu", "phone": "+1 555 0100"}',
+            ).run()
+            return await handler
+
+        result = asyncio.run(run_agent())
+
+        self.assertTrue(result["success"])
+        self.assertIsInstance(result["structured_output"], ContactInfo)
+        self.assertEqual(result["structured_output"].name, "Grace Liu")
+
+    def test_structured_output_agent_reports_missing_llm_for_plain_text(self):
+        async def run_agent():
+            handler = StructuredOutputAgent(
+                llm=None,
+                pydantic_model=ContactInfo,
+                answer_text="Grace Liu can be reached at +1 555 0100.",
+            ).run()
+            return await handler
+
+        result = asyncio.run(run_agent())
+
+        self.assertFalse(result["success"])
+        self.assertIsNone(result["structured_output"])
+        self.assertIn("No structured output LLM", result["error_message"])
+
+
+class MobileAgentOutputSchemaTest(unittest.TestCase):
+    def test_no_schema_keeps_unstructured_mode(self):
+        config = MobileConfig.from_dict({"agent": {"name": "external-agent"}})
+        agent = MobileAgent("Find contact info", config=config)
+
+        self.assertIsNone(agent.output_model)
+        self.assertIsNone(agent.structured_output_llm)
+
+    def test_set_output_schema_configures_model(self):
+        config = MobileConfig.from_dict({"agent": {"name": "external-agent"}})
+        agent = MobileAgent("Find contact info", config=config)
+
+        returned = agent.set_output_schema(ContactInfo)
+
+        self.assertIs(returned, agent)
+        self.assertIs(agent.output_model, ContactInfo)
+
+    def test_set_output_schema_rejects_non_model(self):
+        config = MobileConfig.from_dict({"agent": {"name": "external-agent"}})
+        agent = MobileAgent("Find contact info", config=config)
+
+        with self.assertRaises(TypeError):
+            agent.set_output_schema(dict)  # type: ignore[arg-type]
+
+
+if __name__ == "__main__":
+    unittest.main()