feat: complete phase 2 -- multi-agent routing, interrupt TTL, escalation, templates

- Intent classification with LLM structured output (single/multi/ambiguous)
- Discount agent with apply_discount and generate_coupon tools
- Interrupt manager with 30-min TTL auto-expiration and retry prompts
- Webhook escalation module with exponential backoff retry (max 3)
- Three vertical industry templates (e-commerce, SaaS, fintech)
- Template loading in AgentRegistry
- Enhanced supervisor prompt with dynamic agent descriptions
- 153 tests passing, 90.18% coverage

backend/app/graph.py

@@ -2,12 +2,14 @@
 
 from __future__ import annotations
 
+import logging
 from typing import TYPE_CHECKING
 
 from langgraph.prebuilt import create_react_agent
 from langgraph_supervisor import create_supervisor
 
 from app.agents import get_tools_by_names
+from app.intent import ClassificationResult, IntentClassifier
 
 if TYPE_CHECKING:
     from langchain_core.language_models import BaseChatModel
@@ -16,15 +18,31 @@ if TYPE_CHECKING:
 
     from app.registry import AgentRegistry
 
+logger = logging.getLogger(__name__)
+
 SUPERVISOR_PROMPT = (
     "You are a customer support supervisor. "
-    "Route customer requests to the appropriate agent based on their description. "
-    "For order status and tracking queries, use the order_lookup agent. "
-    "For order modifications like cancellations, use the order_actions agent. "
-    "For anything else, use the fallback agent."
+    "Route customer requests to the appropriate agent based on their description.\n\n"
+    "Available agents and their roles:\n"
+    "{agent_descriptions}\n\n"
+    "Routing rules:\n"
+    "- For order status and tracking queries, use the order_lookup agent.\n"
+    "- For order modifications like cancellations, use the order_actions agent.\n"
+    "- For discounts, promotions, or coupon codes, use the discount agent.\n"
+    "- For anything else or when uncertain, use the fallback agent.\n"
+    "- If the user's request involves multiple actions, execute them in order.\n"
+    "- If a previous intent classification is provided, follow it.\n"
 )
 
 
+def _format_agent_descriptions(registry: AgentRegistry) -> str:
+    """Build agent description text for the supervisor prompt."""
+    lines = []
+    for agent in registry.list_agents():
+        lines.append(f"- {agent.name}: {agent.description}")
+    return "\n".join(lines)
+
+
 def build_agent_nodes(
     registry: AgentRegistry,
     llm: BaseChatModel,
@@ -56,15 +74,48 @@ def build_graph(
     registry: AgentRegistry,
     llm: BaseChatModel,
     checkpointer: AsyncPostgresSaver,
+    intent_classifier: IntentClassifier | None = None,
 ) -> CompiledStateGraph:
-    """Build and compile the LangGraph supervisor graph."""
+    """Build and compile the LangGraph supervisor graph.
+
+    If an intent_classifier is provided, the supervisor prompt is enhanced
+    with agent descriptions for better routing. The classifier is stored
+    for use by the routing layer (ws_handler).
+    """
     agent_nodes = build_agent_nodes(registry, llm)
+    agent_descriptions = _format_agent_descriptions(registry)
+
+    prompt = SUPERVISOR_PROMPT.format(agent_descriptions=agent_descriptions)
 
     workflow = create_supervisor(
         agent_nodes,
         model=llm,
-        prompt=SUPERVISOR_PROMPT,
+        prompt=prompt,
         output_mode="full_history",
     )
 
-    return workflow.compile(checkpointer=checkpointer)
+    graph = workflow.compile(checkpointer=checkpointer)
+
+    # Attach classifier and registry to graph for use by ws_handler
+    graph.intent_classifier = intent_classifier  # type: ignore[attr-defined]
+    graph.agent_registry = registry  # type: ignore[attr-defined]
+
+    return graph
+
+
+async def classify_intent(
+    graph: CompiledStateGraph,
+    message: str,
+) -> ClassificationResult | None:
+    """Classify user intent using the graph's attached classifier.
+
+    Returns None if no classifier is configured.
+    """
+    classifier = getattr(graph, "intent_classifier", None)
+    registry = getattr(graph, "agent_registry", None)
+
+    if classifier is None or registry is None:
+        return None
+
+    agents = registry.list_agents()
+    return await classifier.classify(message, agents)