fix(workflows): dispatch agent nodes by provider, not display label

/api/models reports `display_provider` when a catalog YAML sets one
(`foundry`, `azure_foundry`, `cloudflare`), and the builder persists that
string as a node's `llm_name`. The engine passed it straight to
LLMCreator, which only knows the names in PROVIDERS_BY_NAME and raises
`No LLM class found for type <label>`.

That fails the node before any LLM call, so the turn ends in ~60ms with
an empty answer and no tokens generated. It hits the *default* path: the
platform-default model's label is stamped into every newly dragged agent
node and validateWorkflow requires an agent node, so a new user's
untouched workflow could not produce a token regardless of what they
typed. Ordinary chat was unaffected because it resolves the provider from
the model registry and never reads the stored name.

Adds `resolve_dispatch_provider`, which prefers a stored name that is a
real dispatch provider, then the registry lookup, then the parent agent.
Nodes already saved with a label are repaired at run time, so no
migration is needed. The api_key now resolves from the normalized name
too — `get_api_key_for_provider` falls back to settings.API_KEY for names
it does not recognize, which would have sent the deployment key to
whatever endpoint the label happened to select.

application/agents/workflows/workflow_engine.py

@@ -291,7 +291,7 @@ class WorkflowEngine:
         from application.core.model_utils import (
             get_api_key_for_provider,
             get_model_capabilities,
-            get_provider_from_model_id,
+            resolve_dispatch_provider,
         )
 
         node_config = AgentNodeConfig(**node.config.get("config", node.config))
@@ -314,13 +314,22 @@ class WorkflowEngine:
             if isinstance(self.agent.decoded_token, dict)
             else None
         )
-        node_llm_name = (
-            node_config.llm_name
-            or get_provider_from_model_id(
-                node_model_id or "", user_id=node_user_id
-            )
-            or self.agent.llm_name
+        # ``node_config.llm_name`` is whatever the builder stored, which for
+        # catalogs with a ``display_provider`` is a presentation label
+        # ("foundry", "azure_foundry", "cloudflare") that no LLM class is
+        # registered under. Trusting it verbatim fails the node before any LLM
+        # call and hands the user a blank answer, so resolve it to a real
+        # dispatch provider first — this also repairs nodes already saved with
+        # a label, without a migration.
+        node_llm_name = resolve_dispatch_provider(
+            node_config.llm_name,
+            node_model_id,
+            user_id=node_user_id,
+            fallback=self.agent.llm_name,
         )
+        # Resolve the key from the normalized name: get_api_key_for_provider
+        # silently returns settings.API_KEY for names it does not know, so a
+        # label here would leak the deployment key to the wrong endpoint.
         node_api_key = get_api_key_for_provider(node_llm_name) or self.agent.api_key
 
         # Structured output gates on the model's registry capability flags;

application/core/model_utils.py

@@ -20,6 +20,52 @@ def get_api_key_for_provider(provider: str) -> Optional[str]:
     return settings.API_KEY
 
 
+def resolve_dispatch_provider(
+    stored_llm_name: Optional[str],
+    model_id: Optional[str] = None,
+    user_id: Optional[str] = None,
+    fallback: Optional[str] = None,
+) -> Optional[str]:
+    """Resolve a stored provider string to one ``LLMCreator`` can dispatch.
+
+    ``/api/models`` reports ``display_provider`` when a catalog YAML sets one
+    (``foundry``, ``azure_foundry``, ``cloudflare``, …), and clients persist
+    that label as ``llm_name`` on agents and workflow nodes. Those labels are
+    presentation-only: they are absent from ``PROVIDERS_BY_NAME``, so passing
+    one to ``LLMCreator.create_llm`` raises ``No LLM class found for type
+    <label>``. Normalizing here keeps already-stored records working without a
+    migration.
+
+    Resolution order: a stored name that is a real dispatch provider wins;
+    otherwise the model registry decides; otherwise ``fallback``.
+
+    Args:
+        stored_llm_name: Provider string persisted on the record, possibly a
+            display label.
+        model_id: Model whose registry entry knows the true provider.
+        user_id: BYOM-resolution scope for per-user model records.
+        fallback: Used when neither the stored name nor the registry resolves.
+
+    Returns:
+        A dispatchable provider name, or ``fallback`` when nothing resolves.
+    """
+    from application.llm.providers import PROVIDERS_BY_NAME
+
+    # Return the *canonical* lowercase name. ``LLMCreator`` lowercases before
+    # its own lookup, but ``get_api_key_for_provider`` matches exactly — so a
+    # stored "OpenAI" would pass this guard and then silently fall through to
+    # ``settings.API_KEY``, which is the key leak this function exists to stop.
+    if stored_llm_name and stored_llm_name.lower() in PROVIDERS_BY_NAME:
+        return stored_llm_name.lower()
+    if model_id:
+        resolved = get_provider_from_model_id(model_id, user_id=user_id)
+        if resolved:
+            return resolved
+    if fallback and fallback.lower() in PROVIDERS_BY_NAME:
+        return fallback.lower()
+    return fallback
+
+
 def get_all_available_models(
     user_id: Optional[str] = None,
 ) -> Dict[str, Dict[str, Any]]:

tests/agents/test_workflow_engine.py

@@ -507,3 +507,96 @@ class TestWorkflowEngineAdditionalCoverage:
         # A non-dict schema triggers JsonSchemaValidationError
         with pytest.raises(ValueError, match="Invalid JSON schema"):
             engine._normalize_node_json_schema("not_a_dict", "TestNode")
+
+
+class TestAgentNodeProviderResolution:
+    """``llm_name`` stored on a node is a *display* label, not a dispatch name.
+
+    ``/api/models`` reports ``display_provider`` (e.g. ``foundry``,
+    ``azure_foundry``, ``cloudflare``) and the builder stores that string on
+    the node. Handing it to ``LLMCreator`` raises ``No LLM class found for
+    type <label>``, which fails the node before any LLM call and returns a
+    blank answer to the user. The engine must resolve the real dispatch
+    provider from the model registry instead.
+    """
+
+    @staticmethod
+    def _run(monkeypatch, node, *, registry_provider="openai_compatible"):
+        """Execute one agent node, returning the kwargs the factory saw."""
+        engine = create_engine()
+        engine.state["query"] = "test"
+        captured = {}
+
+        def _capture(**kwargs):
+            captured.update(kwargs)
+            return StubNodeAgent([{"answer": "ok"}])
+
+        monkeypatch.setattr(
+            WorkflowNodeAgentFactory, "create", staticmethod(_capture)
+        )
+        monkeypatch.setattr(
+            "application.core.model_utils.get_api_key_for_provider",
+            lambda name: f"key-for-{name}",
+        )
+        monkeypatch.setattr(
+            "application.core.model_utils.get_provider_from_model_id",
+            lambda _, **_kwargs: registry_provider,
+        )
+        monkeypatch.setattr(
+            "application.core.model_utils.get_model_capabilities",
+            lambda _, **_kwargs: None,
+        )
+        list(engine._execute_agent_node(node))
+        return captured
+
+    @pytest.mark.parametrize(
+        "display_label", ["azure_foundry", "cloudflare", "foundry"]
+    )
+    def test_display_provider_label_resolves_to_dispatch_provider(
+        self, monkeypatch, display_label
+    ):
+        """A display label must not reach LLMCreator (err#33)."""
+        node = create_agent_node(node_id="n1")
+        node.config["model_id"] = "Kimi-K2.6"
+        node.config["llm_name"] = display_label
+
+        captured = self._run(monkeypatch, node)
+
+        assert captured["llm_name"] == "openai_compatible"
+        # The api_key must follow the *normalized* name: resolving against the
+        # display label silently falls through to settings.API_KEY.
+        assert captured["api_key"] == "key-for-openai_compatible"
+
+    def test_real_provider_name_is_preserved(self, monkeypatch):
+        """A node storing a genuine dispatch name keeps it."""
+        node = create_agent_node(node_id="n2")
+        node.config["model_id"] = "gpt-4o"
+        node.config["llm_name"] = "openai"
+
+        captured = self._run(monkeypatch, node, registry_provider="openai")
+
+        assert captured["llm_name"] == "openai"
+
+    def test_unresolvable_label_falls_back_to_parent_agent(self, monkeypatch):
+        """No registry hit: inherit the parent agent rather than dispatching junk."""
+        node = create_agent_node(node_id="n3")
+        node.config["model_id"] = "mystery-model"
+        node.config["llm_name"] = "some_unknown_label"
+
+        captured = self._run(monkeypatch, node, registry_provider=None)
+
+        # create_engine()'s parent agent is llm_name="openai"
+        assert captured["llm_name"] == "openai"
+
+    def test_retriever_config_uses_normalized_provider(self, monkeypatch):
+        """The agentic retriever path builds its own kwargs — normalize there too."""
+        node = create_agent_node(node_id="n4")
+        node.config["model_id"] = "Kimi-K2.6"
+        node.config["llm_name"] = "azure_foundry"
+        node.config["agent_type"] = "agentic"
+        node.config["sources"] = ["src-1"]
+
+        captured = self._run(monkeypatch, node)
+
+        assert captured["retriever_config"]["llm_name"] == "openai_compatible"
+        assert captured["retriever_config"]["api_key"] == "key-for-openai_compatible"