diff --git a/letta/server/rest_api/routers/v1/messages.py b/letta/server/rest_api/routers/v1/messages.py
index 047d8f5e..5424edda 100644
--- a/letta/server/rest_api/routers/v1/messages.py
+++ b/letta/server/rest_api/routers/v1/messages.py
@@ -7,7 +7,7 @@ from starlette.requests import Request
 from letta.agents.letta_agent_batch import LettaAgentBatch
 from letta.log import get_logger
 from letta.orm.errors import NoResultFound
-from letta.schemas.job import BatchJob, JobStatus, JobType
+from letta.schemas.job import BatchJob, JobStatus, JobType, JobUpdate
 from letta.schemas.letta_request import CreateBatch
 from letta.server.rest_api.utils import get_letta_server
 from letta.server.server import SyncServer
@@ -43,18 +43,17 @@ async def create_messages_batch(
         if length > max_bytes:
             raise HTTPException(status_code=413, detail=f"Request too large ({length} bytes). Max is {max_bytes} bytes.")
 
-    try:
-        actor = server.user_manager.get_user_or_default(user_id=actor_id)
+    actor = server.user_manager.get_user_or_default(user_id=actor_id)
+    batch_job = BatchJob(
+        user_id=actor.id,
+        status=JobStatus.running,
+        metadata={
+            "job_type": "batch_messages",
+        },
+        callback_url=str(payload.callback_url),
+    )
 
-        # Create a new job
-        batch_job = BatchJob(
-            user_id=actor.id,
-            status=JobStatus.created,
-            metadata={
-                "job_type": "batch_messages",
-            },
-            callback_url=str(payload.callback_url),
-        )
+    try:
         batch_job = server.job_manager.create_job(pydantic_job=batch_job, actor=actor)
 
         # create the batch runner
@@ -68,7 +67,7 @@ async def create_messages_batch(
             job_manager=server.job_manager,
             actor=actor,
         )
-        llm_batch_job = await batch_runner.step_until_request(batch_requests=payload.requests, letta_batch_job_id=batch_job.id)
+        await batch_runner.step_until_request(batch_requests=payload.requests, letta_batch_job_id=batch_job.id)
 
         # TODO: update run metadata
     except Exception as e:
@@ -78,7 +77,7 @@ async def create_messages_batch(
         traceback.print_exc()
 
         # mark job as failed
-        server.job_manager.update_job_by_id(job_id=batch_job.id, job=BatchJob(status=JobStatus.failed))
+        server.job_manager.update_job_by_id(job_id=batch_job.id, job=BatchJob(status=JobStatus.failed), actor=actor)
         raise
     return batch_job
 
@@ -129,8 +128,19 @@ async def cancel_batch_run(
 
     try:
         job = server.job_manager.get_job_by_id(job_id=batch_id, actor=actor)
-        job.status = JobStatus.cancelled
-        server.job_manager.update_job_by_id(job_id=job, job=job)
-        # TODO: actually cancel it
+        job = server.job_manager.update_job_by_id(job_id=job.id, job_update=JobUpdate(status=JobStatus.cancelled), actor=actor)
+
+        # Get related llm batch jobs
+        llm_batch_jobs = server.batch_manager.list_llm_batch_jobs(letta_batch_id=job.id, actor=actor)
+        for llm_batch_job in llm_batch_jobs:
+            if llm_batch_job.status in {JobStatus.running, JobStatus.created}:
+                # TODO: Extend to providers beyond anthropic
+                # TODO: For now, we only support anthropic
+                # Cancel the job
+                anthropic_batch_id = llm_batch_job.create_batch_response.id
+                await server.anthropic_async_client.messages.batches.cancel(anthropic_batch_id)
+
+                # Update all the batch_job statuses
+                server.batch_manager.update_llm_batch_status(llm_batch_id=llm_batch_job.id, status=JobStatus.cancelled, actor=actor)
     except NoResultFound:
         raise HTTPException(status_code=404, detail="Run not found")
diff --git a/tests/integration_test_batch.py b/tests/integration_test_batch.py
index d89a3a02..d36c5422 100644
--- a/tests/integration_test_batch.py
+++ b/tests/integration_test_batch.py
@@ -7,9 +7,23 @@ from dotenv import load_dotenv
 from letta_client import Letta, LettaBatchRequest, MessageCreate, TextContent
 
 from letta.config import LettaConfig
+from letta.jobs.llm_batch_job_polling import poll_running_llm_batches
+from letta.orm import Base
+from letta.schemas.enums import JobStatus
+from letta.server.db import db_context
 from letta.server.server import SyncServer
 
 
+@pytest.fixture(autouse=True)
+def clear_batch_tables():
+    """Clear batch-related tables before each test."""
+    with db_context() as session:
+        for table in reversed(Base.metadata.sorted_tables):
+            if table.name in {"jobs", "llm_batch_job", "llm_batch_items"}:
+                session.execute(table.delete())  # Truncate table
+        session.commit()
+
+
 def run_server():
     """Starts the Letta server in a background thread."""
     load_dotenv()
@@ -54,7 +68,8 @@ def client(server_url):
     return Letta(base_url=server_url)
 
 
-def test_create_batch(client: Letta):
+@pytest.mark.asyncio
+async def test_create_batch(client: Letta, server: SyncServer):
 
     # create agents
     agent1 = client.agents.create(
@@ -105,11 +120,21 @@ def test_create_batch(client: Letta):
 
     # list batches
     batches = client.batches.list()
-    assert len(batches) > 0, f"Expected 1 batch, got {len(batches)}"
+    assert len(batches) == 1, f"Expected 1 batch, got {len(batches)}"
+    assert batches[0].status == JobStatus.running
+
+    # Poll it once
+    await poll_running_llm_batches(server)
 
     # get the batch results
     results = client.batches.retrieve(
         batch_id=run.id,
     )
     assert results is not None
-    print(results)
+
+    # cancel
+    client.batches.cancel(batch_id=run.id)
+    batch_job = client.batches.retrieve(
+        batch_id=run.id,
+    )
+    assert batch_job.status == JobStatus.cancelled