fix: handle LLM and graph errors in weather agent executor

The execute() method had no error handling around get_graph() and
graph.astream(). When the LLM call fails (bad API key, model
unavailable, network error), the unhandled exception left the A2A
task stuck in "submitted" state with no artifacts and no error
message — making failures invisible to both tests and users.

Now graph initialization and LLM execution errors are caught and
reported as failed tasks with the actual error message, matching
the existing pattern for MCP connection failures.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: Paolo Dettori <dettori@us.ibm.com>

Author: pdettori

a2a/weather_service/src/weather_service/agent.py

@@ -130,18 +130,30 @@ async def execute(self, context: RequestContext, event_queue: EventQueue):
             await event_emitter.emit_event(f"Error: Cannot connect to MCP weather service at {os.getenv('MCP_URL', 'http://localhost:8000/sse')}. Please ensure the weather MCP server is running. Error: {tool_error}", failed=True)
             return
 
-        graph = await get_graph(mcpclient)
-        async for event in graph.astream(input, stream_mode="updates"):
-            await event_emitter.emit_event(
-                "\n".join(
-                    f"🚶‍♂️{key}: {str(value)[:256] + '...' if len(str(value)) > 256 else str(value)}"
-                    for key, value in event.items()
+        try:
+            graph = await get_graph(mcpclient)
+        except Exception as graph_error:
+            logger.error(f'Failed to create LLM graph: {graph_error}')
+            await event_emitter.emit_event(f"Error: Failed to initialize LLM graph: {graph_error}", failed=True)
+            return
+
+        try:
+            async for event in graph.astream(input, stream_mode="updates"):
+                await event_emitter.emit_event(
+                    "\n".join(
+                        f"🚶‍♂️{key}: {str(value)[:256] + '...' if len(str(value)) > 256 else str(value)}"
+                        for key, value in event.items()
+                    )
+                    + "\n"
                 )
-                + "\n"
-            )
-            output = event
-            logger.info(f'event: {event}')
-        output = output.get("assistant", {}).get("final_answer")
+                output = event
+                logger.info(f'event: {event}')
+        except Exception as llm_error:
+            logger.error(f'LLM execution failed: {llm_error}')
+            await event_emitter.emit_event(f"Error: LLM execution failed: {llm_error}", failed=True)
+            return
+
+        output = output.get("assistant", {}).get("final_answer") if output else None
 
         # Set span output BEFORE emitting final event (for streaming response capture)
         # This populates mlflow.spanOutputs, output.value, gen_ai.completion