diff --git a/agents/rag/setup_corpus.py b/agents/rag/setup_corpus.py
index 37ad29f..8fd5bb4 100644
--- a/agents/rag/setup_corpus.py
+++ b/agents/rag/setup_corpus.py
@@ -6,6 +6,13 @@ Project: propane-will-491900-m5
 SDK 1.153.1 has a bug where backend_config crashes when provided,
 and defaults to Spanner when omitted. We bypass it by calling the
 REST API directly for corpus creation, then use the SDK for everything else.
+
+Key design decisions:
+- ragEngineConfig PATCH always targets us-central1 — it is a project-level
+  control-plane endpoint that does NOT exist in other regions.
+- Corpus creation payload omits vectorDbConfig entirely. Sending
+  vectorDbConfig.ragManagedDb forces Spanner mode which is allowlist-restricted
+  for new projects. Omitting it honours the project-level basic (serverless) config.
 """
 
 import os
@@ -25,6 +32,10 @@ GCS_SOURCE = os.environ.get(
     f"gs://{PROJECT_ID}-agent-staging/rag-docs/",
 )
 
+# ragEngineConfig is a project-level control-plane endpoint.
+# It only exists in us-central1 regardless of where the corpus lives.
+_ENGINE_CONFIG_LOCATION = "us-central1"
+
 
 def get_token() -> str:
     return subprocess.check_output(
@@ -35,12 +46,14 @@ def get_token() -> str:
 def ensure_serverless_engine_config() -> None:
     """
     Set project-level RAG Engine Config to basic (serverless) tier.
-    Polls the operation until done.
+    Always targets us-central1 — the ragEngineConfig endpoint is global/control-plane
+    and does not exist in other regions (patching europe-west1 etc. returns 'Invalid
+    endpoint name').
     """
     print("Ensuring RAG Engine Config is set to serverless (basic) tier...")
     endpoint = (
-        f"https://{LOCATION}-aiplatform.googleapis.com/v1beta1"
-        f"/projects/{PROJECT_ID}/locations/{LOCATION}/ragEngineConfig"
+        f"https://{_ENGINE_CONFIG_LOCATION}-aiplatform.googleapis.com/v1beta1"
+        f"/projects/{PROJECT_ID}/locations/{_ENGINE_CONFIG_LOCATION}/ragEngineConfig"
     )
     payload = json.dumps({"ragManagedDbConfig": {"basic": {}}})
     token = get_token()
@@ -62,7 +75,9 @@ def ensure_serverless_engine_config() -> None:
     op_name = resp.get("name", "")
     if "/operations/" in op_name and not resp.get("done"):
         print(f"  Polling operation {op_name.split('/')[-1]}...")
-        op_url = f"https://{LOCATION}-aiplatform.googleapis.com/v1beta1/{op_name}"
+        op_url = (
+            f"https://{_ENGINE_CONFIG_LOCATION}-aiplatform.googleapis.com/v1beta1/{op_name}"
+        )
         for _ in range(20):
             time.sleep(3)
             token = get_token()
@@ -84,6 +99,11 @@ def create_corpus_rest() -> str:
     """
     Create corpus via REST API directly, bypassing SDK backend_config bug.
     Returns the corpus resource name.
+
+    IMPORTANT: vectorDbConfig is intentionally omitted from the payload.
+    Sending vectorDbConfig.ragManagedDb explicitly requests Spanner mode,
+    which is allowlist-restricted for new projects. Omitting it causes the
+    API to honour the project-level ragEngineConfig (basic/serverless).
     """
     url = (
         f"https://{LOCATION}-aiplatform.googleapis.com/v1beta1"
@@ -95,10 +115,8 @@ def create_corpus_rest() -> str:
             "vertexPredictionEndpoint": {
                 "model": "publishers/google/models/text-embedding-004"
             }
-        },
-        "vectorDbConfig": {
-            "ragManagedDb": {}
         }
+        # vectorDbConfig intentionally omitted — see docstring above.
     })
     token = get_token()
     result = subprocess.run(