update comments

Author: Fr4nc3

src/backend/kernel_agents/agent_factory.py

@@ -121,19 +121,31 @@ async def create_agent(
     ) -> BaseAgent:
         """Create an agent of the specified type.
 
+        This method creates and initializes an agent instance of the specified type. If an agent
+        of the same type already exists for the session, it returns the cached instance. The method
+        handles the complete initialization process including:
+        1. Creating a memory store for the agent
+        2. Setting up the Semantic Kernel
+        3. Loading appropriate tools from JSON configuration files
+        4. Creating an Azure AI agent definition using the AI Project client
+        5. Initializing the agent with all required parameters
+        6. Running any asynchronous initialization if needed
+        7. Caching the agent for future use
+
         Args:
-            agent_type: The type of agent to create
-            session_id: The session ID
-            user_id: The user ID
-            temperature: The temperature to use for the agent
-            system_message: Optional system message for the agent
+            agent_type: The type of agent to create (from AgentType enum)
+            session_id: The unique identifier for the current session
+            user_id: The user identifier for the current user
+            temperature: The temperature parameter for the agent's responses (0.0-1.0)
+            system_message: Optional custom system message to override default
+            response_format: Optional response format configuration for structured outputs
             **kwargs: Additional parameters to pass to the agent constructor
 
         Returns:
-            An instance of the specified agent type
+            An initialized instance of the specified agent type
 
         Raises:
-            ValueError: If the agent type is unknown
+            ValueError: If the agent type is unknown or initialization fails
         """
         # Check if we already have an agent in the cache
         if (
@@ -316,15 +328,23 @@ async def _load_tools_for_agent(
     async def create_all_agents(
         cls, session_id: str, user_id: str, temperature: float = 0.0
     ) -> Dict[AgentType, BaseAgent]:
-        """Create all agent types for a session.
+        """Create all agent types for a session in a specific order.
+
+        This method creates all agent instances for a session in a multi-phase approach:
+        1. First, it creates all basic agent types except for the Planner and GroupChatManager
+        2. Then it creates the Planner agent, providing it with references to all other agents
+        3. Finally, it creates the GroupChatManager with references to all agents including the Planner
+
+        This ordered creation ensures that dependencies between agents are properly established,
+        particularly for the Planner and GroupChatManager which need to coordinate other agents.
 
         Args:
-            session_id: The session ID
-            user_id: The user ID
-            temperature: The temperature to use for the agents
+            session_id: The unique identifier for the current session
+            user_id: The user identifier for the current user
+            temperature: The temperature parameter for agent responses (0.0-1.0)
 
         Returns:
-            Dictionary mapping agent types to agent instances
+            Dictionary mapping agent types (from AgentType enum) to initialized agent instances
         """
 
         # Create each agent type in two phases

src/backend/kernel_agents/human_agent.py

@@ -80,11 +80,16 @@ def __init__(
     async def handle_human_feedback(self, human_feedback: HumanFeedback) -> str:
         """Handle human feedback on a step.
 
+        This method processes feedback provided by a human user on a specific step in a plan.
+        It updates the step with the feedback, marks the step as completed, and notifies the
+        GroupChatManager by creating an ApprovalRequest in the memory store.
+
         Args:
-            kernel_arguments: Contains the human_feedback_json string
+            human_feedback: The HumanFeedback object containing feedback details
+                           including step_id, session_id, and human_feedback text
 
         Returns:
-            Status message
+            Status message indicating success or failure of processing the feedback
         """
 
         # Get the step
@@ -154,11 +159,16 @@ async def provide_clarification(
     ) -> str:
         """Provide clarification on a plan.
 
+        This method stores human clarification information for a plan associated with a session.
+        It retrieves the plan from memory, updates it with the clarification text, and records
+        the event in telemetry.
+
         Args:
-            kernel_arguments: Contains session_id and clarification_text
+            human_clarification: The HumanClarification object containing the session_id
+                                and clarification_text provided by the human user
 
         Returns:
-            Status message
+            Status message indicating success or failure of adding the clarification
         """
         session_id = human_clarification.session_id
         clarification_text = human_clarification.clarification_text