moved observability because it is not agent specific and reordered agent pages

Author: eavanvalkenburg

agent-framework/TOC.yml

@@ -20,6 +20,8 @@ items:
     href: user-guide/hosting/TOC.yml
   - name: DevUI
     href: user-guide/devui/TOC.yml
+  - name: Observability
+    href: user-guide/observability.md
 - name: Integrations
   items:
   - name: AG-UI

agent-framework/user-guide/agents/TOC.yml

@@ -2,17 +2,15 @@
   href: agent-types/TOC.yml
 - name: Running Agents
   href: running-agents.md
-- name: Agent Tools
-  href: agent-tools.md
 - name: Multi-Turn Conversations and Threading
   href: multi-turn-conversation.md
-- name: Agent Middleware
-  href: agent-middleware.md
-- name: Agent Retrieval Augmented Generation (RAG)
-  href: agent-rag.md
 - name: Agent Chat History and Memory
   href: agent-memory.md
-- name: Agent Observability
-  href: agent-observability.md
+- name: Agent Tools
+  href: agent-tools.md
+- name: Agent Retrieval Augmented Generation (RAG)
+  href: agent-rag.md
+- name: Agent Middleware
+  href: agent-middleware.md
 - name: Agent Background Responses
   href: agent-background-responses.md

agent-framework/user-guide/agents/agent-memory.md

@@ -244,21 +244,21 @@ class DatabaseMessageStore(ChatMessageStoreProtocol):
     def __init__(self, connection_string: str):
         self.connection_string = connection_string
         self._messages: list[ChatMessage] = []
-    
+
     async def add_messages(self, messages: Sequence[ChatMessage]) -> None:
         """Add messages to database."""
         # Implement database insertion logic
         self._messages.extend(messages)
-    
+
     async def list_messages(self) -> list[ChatMessage]:
         """Retrieve messages from database."""
         # Implement database query logic
         return self._messages
-    
+
     async def serialize(self, **kwargs: Any) -> Any:
         """Serialize store state for persistence."""
         return {"connection_string": self.connection_string}
-    
+
     async def update_from_state(self, serialized_store_state: Any, **kwargs: Any) -> None:
         """Update store from serialized state."""
         if serialized_store_state:
@@ -280,15 +280,15 @@ from typing import Any
 class UserPreferencesMemory(ContextProvider):
     def __init__(self):
         self.preferences = {}
-    
+
     async def invoking(self, messages: ChatMessage | MutableSequence[ChatMessage], **kwargs: Any) -> Context:
         """Provide user preferences before each invocation."""
         if self.preferences:
             preferences_text = ", ".join([f"{k}: {v}" for k, v in self.preferences.items()])
             instructions = f"User preferences: {preferences_text}"
             return Context(instructions=instructions)
         return Context()
-    
+
     async def invoked(
         self,
         request_messages: ChatMessage | Sequence[ChatMessage],
@@ -357,4 +357,4 @@ await agent.run("What's my name?", thread=restored_thread)
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Agent Observability](./agent-observability.md)
+> [Agent Tools](./agent-tools.md)

agent-framework/user-guide/agents/agent-middleware.md

@@ -194,10 +194,10 @@ async def logging_agent_middleware(
     """Agent middleware that logs execution timing."""
     # Pre-processing: Log before agent execution
     print("[Agent] Starting execution")
-    
+
     # Continue to next middleware or agent execution
     await next(context)
-    
+
     # Post-processing: Log after agent execution
     print("[Agent] Execution completed")
 ```
@@ -225,10 +225,10 @@ async def logging_function_middleware(
     """Function middleware that logs function execution."""
     # Pre-processing: Log before function execution
     print(f"[Function] Calling {context.function.name}")
-    
+
     # Continue to next middleware or function execution
     await next(context)
-    
+
     # Post-processing: Log after function execution
     print(f"[Function] {context.function.name} completed")
 ```
@@ -258,10 +258,10 @@ async def logging_chat_middleware(
     """Chat middleware that logs AI interactions."""
     # Pre-processing: Log before AI call
     print(f"[Chat] Sending {len(context.messages)} messages to AI")
-    
+
     # Continue to next middleware or AI service
     await next(context)
-    
+
     # Post-processing: Log after AI response
     print("[Chat] AI response received")
 ```
@@ -312,18 +312,18 @@ from agent_framework import AgentMiddleware, AgentRunContext
 
 class LoggingAgentMiddleware(AgentMiddleware):
     """Agent middleware that logs execution."""
-    
+
     async def process(
         self,
         context: AgentRunContext,
         next: Callable[[AgentRunContext], Awaitable[None]],
     ) -> None:
         # Pre-processing: Log before agent execution
         print("[Agent Class] Starting execution")
-        
+
         # Continue to next middleware or agent execution
         await next(context)
-        
+
         # Post-processing: Log after agent execution
         print("[Agent Class] Execution completed")
 ```
@@ -337,18 +337,18 @@ from agent_framework import FunctionMiddleware, FunctionInvocationContext
 
 class LoggingFunctionMiddleware(FunctionMiddleware):
     """Function middleware that logs function execution."""
-    
+
     async def process(
         self,
         context: FunctionInvocationContext,
         next: Callable[[FunctionInvocationContext], Awaitable[None]],
     ) -> None:
         # Pre-processing: Log before function execution
         print(f"[Function Class] Calling {context.function.name}")
-        
+
         # Continue to next middleware or function execution
         await next(context)
-        
+
         # Post-processing: Log after function execution
         print(f"[Function Class] {context.function.name} completed")
 ```
@@ -362,18 +362,18 @@ from agent_framework import ChatMiddleware, ChatContext
 
 class LoggingChatMiddleware(ChatMiddleware):
     """Chat middleware that logs AI interactions."""
-    
+
     async def process(
         self,
         context: ChatContext,
         next: Callable[[ChatContext], Awaitable[None]],
     ) -> None:
         # Pre-processing: Log before AI call
         print(f"[Chat Class] Sending {len(context.messages)} messages to AI")
-        
+
         # Continue to next middleware or AI service
         await next(context)
-        
+
         # Post-processing: Log after AI response
         print("[Chat Class] AI response received")
 ```
@@ -398,18 +398,18 @@ async with AzureAIAgentClient(async_credential=credential).create_agent(
         TimingFunctionMiddleware(),  # Applies to all runs
     ],
 ) as agent:
-    
+
     # This run uses agent-level middleware only
     result1 = await agent.run("What's the weather in Seattle?")
-    
+
     # This run uses agent-level + run-level middleware
     result2 = await agent.run(
         "What's the weather in Portland?",
         middleware=[  # Run-level middleware (this run only)
             logging_chat_middleware,
         ]
     )
-    
+
     # This run uses agent-level middleware only (no run-level)
     result3 = await agent.run("What's the weather in Vancouver?")
 ```
@@ -436,7 +436,7 @@ async def blocking_middleware(
             print("Request blocked by middleware")
             context.terminate = True
             return
-    
+
     # If no issues, continue normally
     await next(context)
 ```
@@ -458,14 +458,14 @@ You can use `context.is_streaming` to differentiate between these scenarios and
 
 ```python
 async def weather_override_middleware(
-    context: AgentRunContext, 
+    context: AgentRunContext,
     next: Callable[[AgentRunContext], Awaitable[None]]
 ) -> None:
     """Middleware that overrides weather results for both streaming and non-streaming."""
-    
+
     # Execute the original agent logic
     await next(context)
-    
+
     # Override results if present
     if context.result is not None:
         custom_message_parts = [
@@ -474,13 +474,13 @@ async def weather_override_middleware(
             "22°C with gentle breezes. ",
             "Great day for outdoor activities!"
         ]
-        
+
         if context.is_streaming:
             # Streaming override
             async def override_stream() -> AsyncIterable[AgentRunResponseUpdate]:
                 for chunk in custom_message_parts:
                     yield AgentRunResponseUpdate(contents=[TextContent(text=chunk)])
-            
+
             context.result = override_stream()
         else:
             # Non-streaming override
@@ -497,4 +497,4 @@ This middleware approach allows you to implement sophisticated response transfor
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Agent Retrieval Augmented Generation (RAG)](./agent-rag.md)
+> [Agent Background Responses](./agent-background-responses.md)

agent-framework/user-guide/agents/agent-rag.md

@@ -297,4 +297,4 @@ Each connector provides the same `create_search_function` method that can be bri
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Agent Chat History and Memory](./agent-memory.md)
+> [Agent Middleware](./agent-middleware.md)

agent-framework/user-guide/agents/agent-tools.md

@@ -162,7 +162,7 @@ result1 = await agent.run(
 
 # Use different tools for different runs
 result2 = await agent.run(
-    "What's the current time?", 
+    "What's the current time?",
     tools=[get_time]  # Different tool for this query
 )
 
@@ -292,4 +292,4 @@ result = await agent.run(
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Multi-turn Conversation](./multi-turn-conversation.md)
+> [Agent Retrieval Augmented Generation](./agent-rag.md)

agent-framework/user-guide/agents/multi-turn-conversation.md

@@ -226,4 +226,4 @@ async def multi_turn_example():
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Agent Middleware](./agent-middleware.md)
+> [Agent Memory](./agent-memory.md)

agent-framework/user-guide/agents/running-agents.md

@@ -80,7 +80,7 @@ Python agents support passing keyword arguments to customize each run. The speci
 Common options for `ChatAgent` include:
 
 - `max_tokens`: Maximum number of tokens to generate
-- `temperature`: Controls randomness in response generation  
+- `temperature`: Controls randomness in response generation
 - `model`: Override the model for this specific run
 - `tools`: Add additional tools for this run only
 - `response_format`: Specify the response format (e.g., structured output)
@@ -171,7 +171,7 @@ of the result contained in the update, and drill into the detail via the `conten
 async for update in agent.run_stream("What is the weather like in Amsterdam?"):
     print(f"Update text: {update.text}")
     print(f"Content count: {len(update.contents)}")
-    
+
     # Access individual content items
     for content in update.contents:
         if hasattr(content, 'text'):
@@ -256,4 +256,4 @@ for message in response.messages:
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Agent Tools](./agent-tools.md)
+> [Multi-Turn Conversations and Threading](./multi-turn-conversation.md)

…user-guide/agents/agent-observability.md …nt-framework/user-guide/observability.mdagent-framework/user-guide/agents/agent-observability.md renamed to agent-framework/user-guide/observability.md agent-framework/user-guide/agents/agent-observability.md renamed to agent-framework/user-guide/observability.md

@@ -1,5 +1,5 @@
 ---
-title: Agent Observability
+title: Observability
 description: Learn how to use observability with Agent Framework
 zone_pivot_groups: programming-languages
 author: eavanvalkenburg
@@ -470,8 +470,3 @@ This trace shows:
 We have a number of samples in our repository that demonstrate these capabilities, see the [observability samples folder](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/observability) on Github. That includes samples for using zero-code telemetry as well.
 
 ::: zone-end
-
-## Next steps
-
-> [!div class="nextstepaction"]
-> [Background Responses](./agent-background-responses.md)