Python: Add MultiAgent Orchestration to README. (#12073)

### Motivation and Context

The new multi-agent orchestration samples are missing from the getting
started with agents README. Add them in with their descriptions. Also,
remove two unused intermediate message callback lists from two samples
that are no longer used.

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### Description

Add samples to README. Clean up some samples.

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### Contribution Checklist

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- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
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Author: moonbox3

python/samples/concepts/agents/chat_completion_agent/chat_completion_agent_message_callback.py

@@ -40,9 +40,6 @@ def get_item_price(
         return "$9.99"
 
 
-intermediate_steps: list[ChatMessageContent] = []
-
-
 # This callback function will be called for each intermediate message
 # Which will allow one to handle FunctionCallContent and FunctionResultContent
 # If the callback is not provided, the agent will return the final response

python/samples/concepts/agents/chat_completion_agent/chat_completion_agent_message_callback_streaming.py

@@ -37,9 +37,6 @@ def get_item_price(
         return "$9.99"
 
 
-intermediate_steps: list[ChatMessageContent] = []
-
-
 # This callback function will be called for each intermediate message
 # Which will allow one to handle FunctionCallContent and FunctionResultContent
 # If the callback is not provided, the agent will return the final response

python/samples/getting_started_with_agents/README.md

@@ -49,6 +49,21 @@ Example|Description
 
 _Note: For details on configuring an Azure AI Agent, please see [here](../getting_started_with_agents/azure_ai_agent/README.md)._
 
+## Multi Agent Orchestration
+
+Example|Description
+---|---
+[step1_concurrent](../getting_started_with_agents/multi_agent_orchestration/step1_concurrent.py)|How to run multiple agents concurrently and manage their output.
+[step1a_concurrent_structure_output](../getting_started_with_agents/multi_agent_orchestration/step1a_concurrent_structure_output.py)|How to run concurrent agents that return structured outputs.
+[step2_sequential](../getting_started_with_agents/multi_agent_orchestration/step2_sequential.py)|How to run agents sequentially where each one depends on the previous.
+[step2a_sequential_cancellation_token](../getting_started_with_agents/multi_agent_orchestration/step2a_sequential_cancellation_token.py)|How to use cancellation tokens in a sequential agent flow.
+[step3_group_chat](../getting_started_with_agents/multi_agent_orchestration/step3_group_chat.py)|How to create a group chat with multiple agents interacting together.
+[step3a_group_chat_human_in_the_loop](../getting_started_with_agents/multi_agent_orchestration/step3a_group_chat_human_in_the_loop.py)|How to include a human participant in a group chat with agents.
+[step3b_group_chat_with_chat_completion_manager](../getting_started_with_agents/multi_agent_orchestration/step3b_group_chat_with_chat_completion_manager.py)|How to manage a group chat with agents using a chat completion manager.
+[step4_handoff](../getting_started_with_agents/multi_agent_orchestration/step4_handoff.py)|How to hand off conversation or tasks from one agent to another.
+[step4a_handoff_structured_inputs](../getting_started_with_agents/multi_agent_orchestration/step4a_handoff_structured_inputs.py)|How to perform structured inputs handoffs between agents.
+
+
 ## OpenAI Assistant Agent
 
 Example|Description

python/samples/getting_started_with_agents/multi_agent_orchestration/step4a_handoff_structure_input.py renamed to python/samples/getting_started_with_agents/multi_agent_orchestration/step4a_handoff_structured_inputs.py

@@ -16,7 +16,7 @@
 specialized in a different area.
 
 The input to the orchestration is not longer a string or a chat message, but a Pydantic
-model (i.e. structure input). The model will get transformed into a chat message before
+model (i.e. structured inputs). The model will get transformed into a chat message before
 being passed to the agents. This allows the orchestration to become more flexible and
 easier reusable.