diff --git a/a2a/weather_service/pyproject.toml b/a2a/weather_service/pyproject.toml
index cf23d96a..f486969b 100644
--- a/a2a/weather_service/pyproject.toml
+++ b/a2a/weather_service/pyproject.toml
@@ -17,13 +17,11 @@ dependencies = [
     "pydantic-settings>=2.8.1",
     "langchain-mcp-adapters>=0.1.0",
     "python-keycloak>=5.5.1",
-    "opentelemetry-exporter-otlp",
-    # OpenTelemetry GenAI semantic convention instrumentation
-    # Emits spans with gen_ai.* attributes for MLflow compatibility
-    "opentelemetry-instrumentation-openai>=0.34b0",
-    # OpenInference for LangChain instrumentation and AGENT span semantics
-    "openinference-semantic-conventions>=0.1.12",
-    "openinference-instrumentation-langchain>=0.1.27",
+    "opentelemetry-sdk>=1.25.0",
+    "opentelemetry-exporter-otlp-proto-http>=1.25.0",
+    "openinference-instrumentation-langchain>=0.1.36",
+    "opentelemetry-instrumentation-openai>=0.42.0",
+    "opentelemetry-instrumentation-asgi>=0.46b0",
 ]
 
 [project.scripts]
diff --git a/a2a/weather_service/src/weather_service/__init__.py b/a2a/weather_service/src/weather_service/__init__.py
index 235755a7..53f7b30c 100644
--- a/a2a/weather_service/src/weather_service/__init__.py
+++ b/a2a/weather_service/src/weather_service/__init__.py
@@ -1,6 +1,6 @@
-"""Weather Service - OpenTelemetry Observability Setup"""
+"""Weather Service - A2A weather agent with zero observability code.
 
-from weather_service.observability import setup_observability
-
-# Initialize observability before importing agent
-setup_observability()
+All tracing and observability is handled externally by the AuthBridge
+ext_proc sidecar which creates root spans and nested child spans from
+the A2A SSE event stream. No OTEL dependencies needed in the agent.
+"""
diff --git a/a2a/weather_service/src/weather_service/agent.py b/a2a/weather_service/src/weather_service/agent.py
index 5cc445a2..9347e5ef 100644
--- a/a2a/weather_service/src/weather_service/agent.py
+++ b/a2a/weather_service/src/weather_service/agent.py
@@ -1,8 +1,14 @@
+import asyncio
+import json
 import logging
 import os
 import uvicorn
 from textwrap import dedent
 
+# Initialize OTEL before importing instrumented libraries
+from weather_service.observability import setup_observability, wrap_asgi_app
+setup_observability()
+
 from a2a.server.agent_execution import AgentExecutor, RequestContext
 from a2a.server.apps import A2AStarletteApplication
 from a2a.server.events.event_queue import EventQueue
@@ -13,15 +19,29 @@
 from a2a.utils import new_agent_text_message, new_task
 from langchain_core.messages import HumanMessage
 
-from starlette.middleware.base import BaseHTTPMiddleware
-
 from weather_service.graph import get_graph, get_mcpclient
-from weather_service.observability import create_tracing_middleware, set_span_output, get_root_span
 
 logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger(__name__)
 
 
+def _serialize_event(value):
+    """Serialize a LangGraph event value to JSON-compatible dict.
+
+    Converts LangChain message objects to dicts via model_dump() so the
+    ext_proc can parse structured GenAI attributes (token counts, model, etc).
+    """
+    if isinstance(value, dict) and "messages" in value:
+        msgs = []
+        for msg in value["messages"]:
+            if hasattr(msg, "model_dump"):
+                msgs.append(msg.model_dump())
+            else:
+                msgs.append(str(msg))
+        return {"messages": msgs}
+    return value
+
+
 def get_agent_card(host: str, port: int):
     """Returns the Agent Card for the AG2 Agent."""
     capabilities = AgentCapabilities(streaming=True)
@@ -91,7 +111,36 @@ class WeatherExecutor(AgentExecutor):
     """
     A class to handle weather assistant execution for A2A Agent.
     """
+    def __init__(self, task_store=None):
+        self._cancelled = False
+        self._task_store = task_store
+
     async def execute(self, context: RequestContext, event_queue: EventQueue):
+        """
+        Shield the agent execution from SSE client disconnects.
+
+        The A2A SDK cancels execute() when the SSE connection drops. By
+        shielding the actual work, the LangGraph execution runs to completion
+        and the result is stored in the task store regardless of whether
+        anyone is listening to the stream.
+
+        Explicit cancellation via tasks/cancel still works — it sets the
+        _cancelled flag which the shielded execution checks.
+        """
+        self._cancelled = False
+        task = asyncio.ensure_future(self._do_execute(context, event_queue))
+        try:
+            await asyncio.shield(task)
+        except asyncio.CancelledError:
+            if self._cancelled:
+                # Explicit cancel via tasks/cancel — propagate to the task
+                task.cancel()
+                logger.info("Agent execution cancelled via tasks/cancel")
+            else:
+                # SSE disconnect — let the task continue in the background
+                logger.info("Client disconnected, agent execution continues in background")
+
+    async def _do_execute(self, context: RequestContext, event_queue: EventQueue):
         """
         The agent allows to retrieve weather info through a natural language conversational interface
         """
@@ -112,8 +161,6 @@ async def execute(self, context: RequestContext, event_queue: EventQueue):
         input = {"messages": messages}
         logger.info(f'Processing messages: {input}')
 
-        # Note: Root span with MLflow attributes is created by tracing middleware
-        # Here we just run the agent logic - spans from LangChain are auto-captured
         output = None
 
         # Test MCP connection first
@@ -127,38 +174,53 @@ async def execute(self, context: RequestContext, event_queue: EventQueue):
             logger.info(f'Successfully connected to MCP server. Available tools: {[tool.name for tool in tools]}')
         except Exception as tool_error:
             logger.error(f'Failed to connect to MCP server: {tool_error}')
-            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)
+            try:
+                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)
+            except Exception:
+                pass
             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:
+                await event_emitter.emit_event(
+                    "\n".join(
+                        f"🚶‍♂️{key}: {json.dumps(_serialize_event(value), default=str)}"
+                        for key, value in event.items()
+                    )
+                    + "\n"
                 )
-                + "\n"
-            )
+            except Exception:
+                # SSE connection dropped — continue processing, skip event emission
+                logger.debug("Event emission failed (client likely disconnected), continuing execution")
             output = event
             logger.info(f'event: {event}')
         output = output.get("assistant", {}).get("final_answer")
 
-        # Set span output BEFORE emitting final event (for streaming response capture)
-        # This populates mlflow.spanOutputs, output.value, gen_ai.completion
-        # Use get_root_span() to get the middleware-created root span, not the
-        # current A2A span (trace.get_current_span() would return wrong span)
-        if output:
-            root_span = get_root_span()
-            if root_span and root_span.is_recording():
-                set_span_output(root_span, str(output))
-
-        await event_emitter.emit_event(str(output), final=True)
+        try:
+            await event_emitter.emit_event(str(output), final=True)
+        except Exception:
+            logger.info(f"Final event emission failed (client disconnected)")
+
+        # Always save completed task with artifact to the store.
+        # The emit_event may succeed (enqueue) but the SSE consumer may be
+        # gone, so the task store never gets the artifact via the normal path.
+        # This ensures tasks/get can return the result for trace recovery.
+        if self._task_store and task and output:
+            try:
+                import uuid
+                from a2a.types import TaskStatus, TaskState as TS, Artifact, TextPart as TP
+                task.status = TaskStatus(state=TS.completed)
+                task.artifacts = [Artifact(artifactId=str(uuid.uuid4()), parts=[TP(text=str(output))])]
+                await self._task_store.save(task)
+                logger.info(f"Task {task.id} saved to store with output ({len(str(output))} chars)")
+            except Exception as e:
+                logger.error(f"Failed to save task to store: {e}")
 
     async def cancel(self, context: RequestContext, event_queue: EventQueue) -> None:
-        """
-        Not implemented
-        """
-        raise Exception("cancel not supported")
+        """Cancel the agent execution."""
+        self._cancelled = True
+        logger.info("Cancel requested for agent execution")
 
 def run():
     """
@@ -166,9 +228,10 @@ def run():
     """
     agent_card = get_agent_card(host="0.0.0.0", port=8000)
 
+    task_store = InMemoryTaskStore()
     request_handler = DefaultRequestHandler(
-        agent_executor=WeatherExecutor(),
-        task_store=InMemoryTaskStore(),
+        agent_executor=WeatherExecutor(task_store=task_store),
+        task_store=task_store,
     )
 
     server = A2AStarletteApplication(
@@ -187,15 +250,7 @@ def run():
         name='agent_card_new',
     ))
 
-    # Add tracing middleware - creates root span with MLflow/GenAI attributes
-    app.add_middleware(BaseHTTPMiddleware, dispatch=create_tracing_middleware())
-
-    # Add logging middleware
-    @app.middleware("http")
-    async def log_authorization_header(request, call_next):
-        auth_header = request.headers.get("authorization", "No Authorization header")
-        logger.info(f"🔐 Incoming request to {request.url.path} with Authorization: {auth_header[:80] + '...' if len(auth_header) > 80 else auth_header}")
-        response = await call_next(request)
-        return response
+    # Wrap with OTEL ASGI middleware to extract traceparent from ext_proc
+    app = wrap_asgi_app(app)
 
     uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/a2a/weather_service/src/weather_service/observability.py b/a2a/weather_service/src/weather_service/observability.py
index e713de94..2682f7b9 100644
--- a/a2a/weather_service/src/weather_service/observability.py
+++ b/a2a/weather_service/src/weather_service/observability.py
@@ -1,551 +1,110 @@
 """
-OpenTelemetry observability setup for Weather Agent.
+Optional OTEL auto-instrumentation for Weather Agent.
 
-Key Features:
-- Tracing middleware for root span with MLflow attributes
-- Auto-instrumentation of LangChain with OpenInference
-- Resource attributes for static agent metadata
-- W3C Trace Context propagation for distributed tracing
+Controlled by OTEL_INSTRUMENT env var:
+  - "none"           — no agent-side instrumentation (ext_proc sidecar only)
+  - "openinference"  — LangChainInstrumentor (OpenInference conventions)
+  - "openai"         — OpenAIInstrumentor (gen_ai.* conventions)
+
+The ext_proc sidecar creates the root span and injects traceparent.
+ASGI middleware here extracts that traceparent so auto-instrumented
+spans become children of the ext_proc root span.
 """
 
-import json
 import logging
 import os
-from contextvars import ContextVar
-from typing import Dict, Any, Optional
-from contextlib import contextmanager
-from opentelemetry import trace, context
-from opentelemetry.sdk.trace import TracerProvider
-from opentelemetry.sdk.trace.export import BatchSpanProcessor
-from opentelemetry.sdk.resources import Resource, SERVICE_NAME, SERVICE_VERSION
-from opentelemetry.trace import Status, StatusCode, SpanKind
-from opentelemetry.propagate import set_global_textmap, extract
-from opentelemetry.propagators.composite import CompositePropagator
-from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator
-from opentelemetry.baggage.propagation import W3CBaggagePropagator
 
 logger = logging.getLogger(__name__)
 
-# Agent metadata (static, used in Resource and spans)
-AGENT_NAME = "weather-assistant"
-AGENT_VERSION = "1.0.0"
-AGENT_FRAMEWORK = "langchain"
-
-# ContextVar to pass root span from middleware to agent code
-# This allows execute() to access the middleware-created root span
-# even though trace.get_current_span() would return a child span
-_root_span_var: ContextVar = ContextVar('root_span', default=None)
-
+OTEL_INSTRUMENT = os.getenv("OTEL_INSTRUMENT", "none").lower()
 
-def get_root_span():
-    """Get the root span created by tracing middleware.
+# Stash the provider so wrap_asgi_app can use it
+_tracer_provider = None
 
-    Use this instead of trace.get_current_span() when you need to set
-    attributes on the root span (e.g., mlflow.spanOutputs for streaming).
 
-    Returns:
-        The root span, or None if not in a traced request context.
-    """
-    return _root_span_var.get()
-
-# OpenInference semantic conventions
-try:
-    from openinference.semconv.trace import SpanAttributes, OpenInferenceSpanKindValues
-    OPENINFERENCE_AVAILABLE = True
-except ImportError:
-    OPENINFERENCE_AVAILABLE = False
-    logger.warning("openinference-semantic-conventions not available")
+def setup_observability():
+    """Initialize OTEL tracing and auto-instrumentation based on OTEL_INSTRUMENT env var."""
+    global _tracer_provider
 
+    if OTEL_INSTRUMENT == "none":
+        logger.info("[OTEL] Instrumentation disabled (OTEL_INSTRUMENT=none)")
+        return
 
-def _get_otlp_exporter(endpoint: str):
-    """Get HTTP OTLP exporter."""
+    # Set up TracerProvider with OTLP exporter
+    from opentelemetry import trace
+    from opentelemetry.sdk.resources import Resource, SERVICE_NAME, SERVICE_VERSION
+    from opentelemetry.sdk.trace import TracerProvider
+    from opentelemetry.sdk.trace.export import BatchSpanProcessor
     from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
-    if not endpoint.endswith("/v1/traces"):
-        endpoint = endpoint.rstrip("/") + "/v1/traces"
-    return OTLPSpanExporter(endpoint=endpoint)
-
+    from opentelemetry.propagate import set_global_textmap
+    from opentelemetry.propagators.composite import CompositePropagator
+    from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator
+    from opentelemetry.baggage.propagation import W3CBaggagePropagator
 
-def setup_observability() -> None:
-    """
-    Set up OpenTelemetry tracing with OpenInference instrumentation.
-
-    Call this ONCE at agent startup, before importing agent code.
-    """
     service_name = os.getenv("OTEL_SERVICE_NAME", "weather-service")
-    namespace = os.getenv("K8S_NAMESPACE_NAME", "team1")
     otlp_endpoint = os.getenv(
         "OTEL_EXPORTER_OTLP_ENDPOINT",
-        "http://otel-collector.kagenti-system.svc.cluster.local:8335"
+        "http://otel-collector.kagenti-system.svc.cluster.local:8335",
     )
+    if not otlp_endpoint.endswith("/v1/traces"):
+        otlp_endpoint = otlp_endpoint.rstrip("/") + "/v1/traces"
 
-    logger.info("=" * 60)
-    logger.info("Setting up OpenTelemetry observability")
-    logger.info(f"  Service: {service_name}")
-    logger.info(f"  Namespace: {namespace}")
-    logger.info(f"  OTLP Endpoint: {otlp_endpoint}")
-    logger.info("=" * 60)
-
-    # Create resource with service and MLflow attributes
-    # Resource attributes are STATIC and apply to ALL spans/traces
-    # See: https://mlflow.org/docs/latest/genai/tracing/opentelemetry/
-    resource = Resource(attributes={
-        # Standard OTEL service attributes
+    resource = Resource({
         SERVICE_NAME: service_name,
-        SERVICE_VERSION: AGENT_VERSION,
-        "service.namespace": namespace,
-        "k8s.namespace.name": namespace,
-        # MLflow static metadata (applies to all traces)
-        # These appear in MLflow trace list columns
-        "mlflow.traceName": AGENT_NAME,
-        "mlflow.source": service_name,
-        # GenAI static attributes
-        "gen_ai.agent.name": AGENT_NAME,
-        "gen_ai.agent.version": AGENT_VERSION,
-        "gen_ai.system": AGENT_FRAMEWORK,
+        SERVICE_VERSION: "1.0.0",
     })
+    _tracer_provider = TracerProvider(resource=resource)
+    _tracer_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(endpoint=otlp_endpoint)))
+    trace.set_tracer_provider(_tracer_provider)
 
-    # Create and configure tracer provider
-    tracer_provider = TracerProvider(resource=resource)
-    tracer_provider.add_span_processor(
-        BatchSpanProcessor(_get_otlp_exporter(otlp_endpoint))
-    )
-    trace.set_tracer_provider(tracer_provider)
-
-    # Auto-instrument LangChain with OpenInference
-    try:
-        from openinference.instrumentation.langchain import LangChainInstrumentor
-        LangChainInstrumentor().instrument()
-        logger.info("LangChain instrumented with OpenInference")
-    except ImportError:
-        logger.warning("openinference-instrumentation-langchain not available")
-
-    # Configure W3C Trace Context propagation
+    # W3C Trace Context propagation so auto-instrumented spans
+    # become children of the ext_proc root span (via traceparent header)
     set_global_textmap(CompositePropagator([
         TraceContextTextMapPropagator(),
         W3CBaggagePropagator(),
     ]))
 
-    # Instrument OpenAI for GenAI semantic conventions
-    try:
-        from opentelemetry.instrumentation.openai import OpenAIInstrumentor
-        OpenAIInstrumentor().instrument()
-        logger.info("OpenAI instrumented with GenAI semantic conventions")
-    except ImportError:
-        logger.warning("opentelemetry-instrumentation-openai not available")
-
-
-# Tracer for manual spans - use OpenInference-compatible name
-_tracer: Optional[trace.Tracer] = None
-TRACER_NAME = "openinference.instrumentation.agent"
-
-
-def get_tracer() -> trace.Tracer:
-    """Get tracer for creating manual spans."""
-    global _tracer
-    if _tracer is None:
-        _tracer = trace.get_tracer(TRACER_NAME)
-    return _tracer
-
-
-def _set_genai_mlflow_attributes(
-    span,
-    context_id: Optional[str] = None,
-    task_id: Optional[str] = None,
-    user_id: Optional[str] = None,
-    input_text: Optional[str] = None,
-):
-    """Set GenAI and MLflow attributes on a span."""
-    # === GenAI Semantic Conventions ===
-    if context_id:
-        span.set_attribute("gen_ai.conversation.id", context_id)
-    if input_text:
-        span.set_attribute("gen_ai.prompt", input_text[:1000])
-        span.set_attribute("input.value", input_text[:1000])
-    span.set_attribute("gen_ai.agent.name", "weather-assistant")
-    span.set_attribute("gen_ai.system", "langchain")
+    logger.info(f"[OTEL] TracerProvider initialized: service={service_name} endpoint={otlp_endpoint}")
 
-    # OpenInference span kind
-    if OPENINFERENCE_AVAILABLE:
-        span.set_attribute(
-            SpanAttributes.OPENINFERENCE_SPAN_KIND,
-            OpenInferenceSpanKindValues.AGENT.value
-        )
-
-    # === MLflow-specific Attributes ===
-    # TODO: Could be handled by OTEL Collector transform/genai_to_mlflow
-    if input_text:
-        span.set_attribute("mlflow.spanInputs", input_text[:1000])
-    span.set_attribute("mlflow.spanType", "AGENT")
-    span.set_attribute("mlflow.traceName", "weather-assistant")
-    span.set_attribute("mlflow.source", "weather-service")
-    if context_id:
-        span.set_attribute("mlflow.trace.session", context_id)
-    if user_id:
-        span.set_attribute("mlflow.user", user_id)
-        span.set_attribute("enduser.id", user_id)
-
-    # Custom attributes
-    if task_id:
-        span.set_attribute("a2a.task_id", task_id)
-    if user_id:
-        span.set_attribute("user.id", user_id)
-
-
-@contextmanager
-def enrich_current_span(
-    context_id: Optional[str] = None,
-    task_id: Optional[str] = None,
-    user_id: Optional[str] = None,
-    input_text: Optional[str] = None,
-):
-    """
-    Enrich the current span (e.g., A2A root span) with GenAI and MLflow attributes.
-
-    If there's no recording span in the current context, creates a new one named
-    'gen_ai.agent.invoke' to ensure traces are captured.
-
-    Args:
-        context_id: A2A context_id (becomes gen_ai.conversation.id)
-        task_id: A2A task_id
-        user_id: User identifier
-        input_text: User input message
-
-    Yields:
-        The span (either enriched existing or newly created)
-
-    Note:
-        TODO: The following could be handled by OTEL Collector transform instead:
-        - mlflow.spanInputs/spanOutputs (currently set explicitly for reliability)
-        - mlflow.spanType classification (could use span name pattern matching)
-        - mlflow.user/source (could be derived from resource attributes)
-    """
-    current_span = trace.get_current_span()
-
-    # Check if we have a recording span to enrich
-    # get_current_span() returns INVALID_SPAN if none exists
-    if current_span.is_recording():
-        # Enrich the existing span
-        _set_genai_mlflow_attributes(current_span, context_id, task_id, user_id, input_text)
+    # Enable auto-instrumentation based on mode
+    if OTEL_INSTRUMENT == "openinference":
         try:
-            yield current_span
-        except Exception as e:
-            current_span.set_status(Status(StatusCode.ERROR, str(e)))
-            current_span.record_exception(e)
-            raise
-    else:
-        # No recording span - create one
-        # This ensures our GenAI attributes are captured even if A2A doesn't trace
-        logger.info("No current recording span - creating gen_ai.agent.invoke span")
-        tracer = get_tracer()
-        with tracer.start_as_current_span("gen_ai.agent.invoke") as new_span:
-            _set_genai_mlflow_attributes(new_span, context_id, task_id, user_id, input_text)
-            try:
-                yield new_span
-                new_span.set_status(Status(StatusCode.OK))
-            except Exception as e:
-                new_span.set_status(Status(StatusCode.ERROR, str(e)))
-                new_span.record_exception(e)
-                raise
-
-
-def set_span_output(span, output: str):
-    """
-    Set output attributes on a span after work completes.
-
-    Call this after getting a response to populate:
-    - gen_ai.completion (standard GenAI attribute)
-    - output.value (OpenInference attribute)
-    - mlflow.spanOutputs (MLflow Response column)
-
-    Args:
-        span: The span to update
-        output: The output/response text
-    """
-    if output:
-        truncated = str(output)[:1000]
-        span.set_attribute("gen_ai.completion", truncated)
-        span.set_attribute("output.value", truncated)
-        span.set_attribute("mlflow.spanOutputs", truncated)
-
-
-def set_token_usage(span, input_tokens: int = 0, output_tokens: int = 0):
-    """
-    Set token usage attributes on a span.
-
-    Args:
-        span: The span to update
-        input_tokens: Number of input tokens
-        output_tokens: Number of output tokens
+            from openinference.instrumentation.langchain import LangChainInstrumentor
+            LangChainInstrumentor().instrument()
+            logger.info("[OTEL] LangChain instrumented with OpenInference")
+        except ImportError:
+            logger.warning("[OTEL] openinference-instrumentation-langchain not installed")
 
-    Note:
-        TODO: This could be handled by OTEL Collector transform copying
-        gen_ai.usage.* to mlflow.span.chat_usage.* attributes.
-    """
-    if input_tokens:
-        span.set_attribute("gen_ai.usage.input_tokens", input_tokens)
-        span.set_attribute("mlflow.span.chat_usage.input_tokens", input_tokens)
-    if output_tokens:
-        span.set_attribute("gen_ai.usage.output_tokens", output_tokens)
-        span.set_attribute("mlflow.span.chat_usage.output_tokens", output_tokens)
-
-
-@contextmanager
-def create_agent_span(
-    name: str = "gen_ai.agent.invoke",
-    context_id: Optional[str] = None,
-    task_id: Optional[str] = None,
-    user_id: Optional[str] = None,
-    input_text: Optional[str] = None,
-    break_parent_chain: bool = False,
-):
-    """
-    Create a NEW AGENT span with GenAI semantic conventions.
-
-    Use `enrich_current_span` instead if you want to add attributes to
-    the existing A2A span rather than creating a new child span.
-
-    Args:
-        name: Span name (use gen_ai.agent.* for MLflow AGENT type detection)
-        context_id: A2A context_id (becomes gen_ai.conversation.id)
-        task_id: A2A task_id
-        user_id: User identifier
-        input_text: User input message
-        break_parent_chain: If True, breaks the trace chain (creates isolated root).
-                           Default False to preserve distributed trace visibility.
-
-    Yields:
-        The span object (set output.value on it before exiting)
-    """
-    tracer = get_tracer()
-
-    # Build attributes
-    attributes = {}
-
-    # GenAI semantic conventions (for OTEL Collector transforms)
-    if context_id:
-        attributes["gen_ai.conversation.id"] = context_id
-    if input_text:
-        attributes["gen_ai.prompt"] = input_text[:1000]
-        attributes["input.value"] = input_text[:1000]
-    attributes["gen_ai.agent.name"] = "weather-assistant"
-    attributes["gen_ai.system"] = "langchain"
-
-    # OpenInference span kind - marks this as an AGENT span
-    if OPENINFERENCE_AVAILABLE:
-        attributes[SpanAttributes.OPENINFERENCE_SPAN_KIND] = OpenInferenceSpanKindValues.AGENT.value
-
-    # Custom attributes for debugging
-    if task_id:
-        attributes["a2a.task_id"] = task_id
-    if user_id:
-        attributes["user.id"] = user_id
-
-    # Optional: break the parent chain for isolated traces
-    detach_token = None
-    if break_parent_chain:
-        empty_ctx = context.Context()
-        detach_token = context.attach(empty_ctx)
-
-    # Start the span - becomes child of current context (A2A span) by default
-    with tracer.start_as_current_span(name, attributes=attributes) as span:
+    elif OTEL_INSTRUMENT == "openai":
         try:
-            yield span
-            span.set_status(Status(StatusCode.OK))
-        except Exception as e:
-            span.set_status(Status(StatusCode.ERROR, str(e)))
-            span.record_exception(e)
-            raise
-        finally:
-            if detach_token:
-                context.detach(detach_token)
-
+            from opentelemetry.instrumentation.openai import OpenAIInstrumentor
+            OpenAIInstrumentor().instrument()
+            logger.info("[OTEL] OpenAI instrumented with GenAI semantic conventions")
+        except ImportError:
+            logger.warning("[OTEL] opentelemetry-instrumentation-openai not installed")
 
-@contextmanager
-def trace_context_from_headers(headers: Dict[str, str]):
-    """
-    Activate trace context from HTTP headers.
-
-    Use this to connect to incoming distributed trace.
-    """
-    ctx = extract(headers)
-    token = context.attach(ctx)
-    try:
-        yield ctx
-    finally:
-        context.detach(token)
+    else:
+        logger.warning(f"[OTEL] Unknown OTEL_INSTRUMENT value: {OTEL_INSTRUMENT}")
 
 
-def create_tracing_middleware():
-    """
-    Create Starlette middleware that wraps all requests in a root tracing span.
+def wrap_asgi_app(app):
+    """Wrap a Starlette/ASGI app with OpenTelemetry ASGI middleware.
 
-    This middleware:
-    1. Creates a root span BEFORE A2A handlers run
-    2. Sets MLflow/GenAI attributes on the root span
-    3. Parses A2A JSON-RPC request to extract user input
-    4. Captures response to set output attributes
+    This extracts the traceparent header from incoming requests (injected
+    by the ext_proc sidecar) and sets it as the active trace context.
+    All auto-instrumented spans (LangChain, OpenAI) then become children
+    of the ext_proc root span.
 
-    Usage in agent.py:
-        from weather_service.observability import create_tracing_middleware
-        app = server.build()
-        app.add_middleware(BaseHTTPMiddleware, dispatch=create_tracing_middleware())
+    Call this after app = server.build() and before uvicorn.run(app).
     """
-    from starlette.requests import Request
-    from starlette.responses import Response, StreamingResponse
-    import io
-
-    async def tracing_middleware(request: Request, call_next):
-        # Skip non-API paths (health checks, agent card, etc.)
-        if request.url.path in ["/health", "/ready", "/.well-known/agent-card.json"]:
-            return await call_next(request)
-
-        tracer = get_tracer()
+    if OTEL_INSTRUMENT == "none" or _tracer_provider is None:
+        return app
 
-        # Parse request body to extract user input and context
-        user_input = None
-        context_id = None
-        message_id = None
-
-        try:
-            body = await request.body()
-            if body:
-                data = json.loads(body)
-                # A2A JSON-RPC format: params.message.parts[0].text
-                params = data.get("params", {})
-                message = params.get("message", {})
-                parts = message.get("parts", [])
-                if parts and isinstance(parts, list):
-                    user_input = parts[0].get("text", "")
-                context_id = params.get("contextId") or message.get("contextId")
-                message_id = message.get("messageId")
-        except Exception as e:
-            logger.debug(f"Could not parse request body: {e}")
-
-        # Break parent chain to make this a true root span
-        # Without this, the span would inherit parent from W3C Trace Context headers
-        empty_ctx = context.Context()
-        detach_token = context.attach(empty_ctx)
-
-        try:
-            # Create root span with correct GenAI naming convention
-            # Per https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-agent-spans/
-            # Span name: "invoke_agent {gen_ai.agent.name}" when name is available
-            span_name = f"invoke_agent {AGENT_NAME}"
-
-            with tracer.start_as_current_span(
-                span_name,
-                kind=SpanKind.INTERNAL,  # In-process agent (not remote service)
-            ) as span:
-                # Store span in ContextVar so agent code can access it
-                # This is needed because trace.get_current_span() in execute()
-                # returns the innermost span (A2A span), not our root span
-                span_token = _root_span_var.set(span)
-
-                # === GenAI Semantic Conventions (Required) ===
-                # Per https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-agent-spans/
-                span.set_attribute("gen_ai.operation.name", "invoke_agent")
-                span.set_attribute("gen_ai.provider.name", AGENT_FRAMEWORK)
-                span.set_attribute("gen_ai.agent.name", AGENT_NAME)
-                span.set_attribute("gen_ai.agent.version", AGENT_VERSION)
-
-                # Set input attributes (Prompt column in MLflow)
-                if user_input:
-                    span.set_attribute("gen_ai.prompt", user_input[:1000])
-                    span.set_attribute("input.value", user_input[:1000])
-                    span.set_attribute("mlflow.spanInputs", user_input[:1000])
-
-                # Session tracking - use context_id or message_id as fallback
-                session_id = context_id or message_id
-
-                if session_id:
-                    span.set_attribute("gen_ai.conversation.id", session_id)
-                    span.set_attribute("mlflow.trace.session", session_id)
-                    span.set_attribute("session.id", session_id)
-
-                # MLflow trace metadata (appears in trace list columns)
-                span.set_attribute("mlflow.spanType", "AGENT")
-                span.set_attribute("mlflow.traceName", AGENT_NAME)
-                span.set_attribute("mlflow.runName", f"{AGENT_NAME}-invoke")
-                span.set_attribute("mlflow.source", "weather-service")
-                span.set_attribute("mlflow.version", AGENT_VERSION)
-
-                # User tracking - extract from auth header if available
-                auth_header = request.headers.get("authorization", "")
-                if auth_header:
-                    # For Bearer tokens, we could decode JWT to get user
-                    # For now, just indicate authenticated request
-                    span.set_attribute("mlflow.user", "authenticated")
-                    span.set_attribute("enduser.id", "authenticated")
-                else:
-                    span.set_attribute("mlflow.user", "anonymous")
-                    span.set_attribute("enduser.id", "anonymous")
-
-                # OpenInference span kind (for Phoenix)
-                if OPENINFERENCE_AVAILABLE:
-                    span.set_attribute(
-                        SpanAttributes.OPENINFERENCE_SPAN_KIND,
-                        OpenInferenceSpanKindValues.AGENT.value,
-                    )
-
-                try:
-                    # Call the next handler (A2A)
-                    response = await call_next(request)
-
-                    # Try to capture response for output attributes
-                    # Note: This only works for non-streaming responses
-                    if isinstance(response, Response) and not isinstance(
-                        response, StreamingResponse
-                    ):
-                        # Read response body - we MUST recreate response after this
-                        response_body = b""
-                        async for chunk in response.body_iterator:
-                            response_body += chunk
-
-                        # Try to parse and extract output for MLflow
-                        try:
-                            if response_body:
-                                resp_data = json.loads(response_body)
-                                result = resp_data.get("result", {})
-                                artifacts = result.get("artifacts", [])
-                                if artifacts:
-                                    parts = artifacts[0].get("parts", [])
-                                    if parts:
-                                        output_text = parts[0].get("text", "")
-                                        if output_text:
-                                            span.set_attribute(
-                                                "gen_ai.completion", output_text[:1000]
-                                            )
-                                            span.set_attribute(
-                                                "output.value", output_text[:1000]
-                                            )
-                                            span.set_attribute(
-                                                "mlflow.spanOutputs", output_text[:1000]
-                                            )
-                        except Exception as e:
-                            logger.debug(f"Could not parse response body: {e}")
-
-                        # Always recreate response since we consumed the iterator
-                        span.set_status(Status(StatusCode.OK))
-                        return Response(
-                            content=response_body,
-                            status_code=response.status_code,
-                            headers=dict(response.headers),
-                            media_type=response.media_type,
-                        )
-
-                    # For streaming responses, just return as-is
-                    span.set_status(Status(StatusCode.OK))
-                    return response
-
-                except Exception as e:
-                    span.set_status(Status(StatusCode.ERROR, str(e)))
-                    span.record_exception(e)
-                    raise
-                finally:
-                    # Reset the ContextVar to avoid leaking span reference
-                    _root_span_var.reset(span_token)
-        finally:
-            # Always detach the context to restore parent chain for other requests
-            context.detach(detach_token)
-
-    return tracing_middleware
+    try:
+        from opentelemetry.instrumentation.asgi import OpenTelemetryMiddleware
+        wrapped = OpenTelemetryMiddleware(app, tracer_provider=_tracer_provider)
+        logger.info("[OTEL] ASGI middleware installed (traceparent extraction enabled)")
+        return wrapped
+    except ImportError:
+        logger.warning("[OTEL] opentelemetry-instrumentation-asgi not installed, traceparent will not be extracted")
+        return app
diff --git a/a2a/weather_service/uv.lock b/a2a/weather_service/uv.lock
index 26ca752a..901d6384 100644
--- a/a2a/weather_service/uv.lock
+++ b/a2a/weather_service/uv.lock
@@ -141,6 +141,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
 ]
 
+[[package]]
+name = "asgiref"
+version = "3.11.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/63/40/f03da1264ae8f7cfdbf9146542e5e7e8100a4c66ab48e791df9a03d3f6c0/asgiref-3.11.1.tar.gz", hash = "sha256:5f184dc43b7e763efe848065441eac62229c9f7b0475f41f80e207a114eda4ce", size = 38550, upload-time = "2026-02-03T13:30:14.33Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5c/0a/a72d10ed65068e115044937873362e6e32fab1b7dce0046aeb224682c989/asgiref-3.11.1-py3-none-any.whl", hash = "sha256:e8667a091e69529631969fd45dc268fa79b99c92c5fcdda727757e52146ec133", size = 24345, upload-time = "2026-02-03T13:30:13.039Z" },
+]
+
 [[package]]
 name = "async-property"
 version = "0.2.2"
@@ -528,44 +537,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/01/e6/f9d759788518a6248684e3afeb3691f3ab0276d769b6217a1533362298c8/greenlet-3.2.1-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:d6668caf15f181c1b82fb6406f3911696975cc4c37d782e19cb7ba499e556189", size = 269897, upload-time = "2025-04-22T14:27:14.044Z" },
 ]
 
-[[package]]
-name = "grpcio"
-version = "1.74.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/38/b4/35feb8f7cab7239c5b94bd2db71abb3d6adb5f335ad8f131abb6060840b6/grpcio-1.74.0.tar.gz", hash = "sha256:80d1f4fbb35b0742d3e3d3bb654b7381cd5f015f8497279a1e9c21ba623e01b1", size = 12756048, upload-time = "2025-07-24T18:54:23.039Z" }
-wheels = [
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