step 5 complete: execution plan builder, template registry, and LRU plan cache

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/core/orchestrator.py

@@ -11,7 +11,7 @@ from langchain_openai import ChatOpenAI
 from app.config.settings import settings
 from app.core.agent_registry import AgentRegistry
 from app.core.agent_registry import registry as _default_registry
-from app.schemas import ChatRequest, ChatResponse, ExecutionPlan, PlanStep
+from app.schemas import ChatRequest, ChatResponse, ExecutionPlan
 
 _FALLBACK_AGENT = "task_agent"
 
@@ -99,22 +99,21 @@ async def route_pipeline(
 
 
 def _build_plan(agent_name: str, message: str) -> ExecutionPlan:
-    """Build a minimal ``ExecutionPlan`` for the resolved agent.
+    """Build an ``ExecutionPlan`` for the resolved agent.
 
-    The full ``ExecutionPlanBuilder`` (with template registry and caching) is
-    implemented in Step 5.  This function produces the single-step baseline
-    plan that the orchestrator returns in ``'plan'`` mode.
+    Uses ``ExecutionPlanBuilder`` with the server-side template registry.
+    If a default template exists for the agent, an LLM step is emitted;
+    otherwise a plain ``handle`` action step is used.
     """
-    return ExecutionPlan(
-        agent=agent_name,
-        steps=[
-            PlanStep(
-                action="handle",
-                prompt_template=f"tpl_{agent_name}_default",
-                variables={"message": message},
-            )
-        ],
-    )
+    from app.core.execution_plan import ExecutionPlanBuilder, template_registry
+
+    template_id = f"tpl_{agent_name}_default"
+    builder = ExecutionPlanBuilder(agent_name)
+    if template_registry.has(template_id):
+        builder.add_llm_step(template_id, {"message": message})
+    else:
+        builder.add_step("handle", {"message": message})
+    return builder.build()
 
 
 async def orchestrate(