acrolinx updates

Author: buzahid

learn-pr/wwl-data-ai/orchestrate-sk-multi-agent-solution/6-knowledge-check.yml

@@ -16,24 +16,24 @@ quiz:
     choices:
       - content: "A human-controlled interface for interacting with AI models" 
         isCorrect: false 
-        explanation: "Incorrect. Agents in the framework are not human-controlled; they are autonomous systems designed to process input and complete tasks."
+        explanation: "Incorrect. Agents in the framework aren't human-controlled; they're autonomous systems designed to process input and complete tasks."
       - content: "A storage system used to manage user data" 
         isCorrect: false 
-        explanation: "Incorrect. Agents do not function as data storage systems; they perform tasks based on user interaction and intent."
+        explanation: "Incorrect. Agents don't function as data storage systems; they perform tasks based on user interaction and intent."
       - content: "An autonomous system that interacts with users and performs specific tasks based on user input and intent." 
         isCorrect: true 
         explanation: "Correct. In the Semantic Kernel Agent Framework, agents are autonomous systems that interact with users, interpret input, and perform tasks tailored to the user's needs."
   - content: "How does the Semantic Kernel Agent Framework select the right agent for a task?" 
     choices:  
       - content: "By random selection" 
         isCorrect: false 
-        explanation: "Incorrect. Agent selection is not random; it is based on analyzing user intent and routing according to specific rules."
+        explanation: "Incorrect. Agent selection isn't random; it's based on analyzing user intent and routing according to specific rules."
       - content: "Based on the user's intent and predefined rules" 
         isCorrect: true 
         explanation: "Correct. The framework uses intent recognition and predefined rules to route queries to the most relevant agent."
       - content: "By the agent's physical location" 
         isCorrect: false 
-        explanation: "Incorrect. Agent selection is not based on physical location but on task relevance and expertise."
+        explanation: "Incorrect. Agent selection isn't based on physical location but on task relevance and expertise."
   - content: "What is the role of the Termination Strategy in the Semantic Kernel Agent Framework?" 
     choices:
       - content: "It determines when a conversation or task should end" 

learn-pr/wwl-data-ai/orchestrate-sk-multi-agent-solution/includes/1-introduction.md

@@ -1,17 +1,15 @@
 AI agents offer a powerful combination of technologies, able to complete tasks with the use of generative AI. However, in some situations, the task required might be larger than is realistic for a single agent. For those scenarios, consider a **multi-agent** solution. A multi-agent solution allows agents to collaborate within the same conversation.
 
-Imagine you are trying to address common DevOps challenges such as monitoring application performance, identifying issues, and deploying fixes. A multi-agent system could consist of four specialized agents working collaboratively:
+Imagine you're trying to address common DevOps challenges such as monitoring application performance, identifying issues, and deploying fixes. A multi-agent system could consist of four specialized agents working collaboratively:
 
 1. The Monitoring Agent continuously ingests logs and metrics, detects anomalies using natural language processing (NLP), and triggers alerts when issues arise.
-1. The Root Cause Analysis Agent then correlates these anomalies with recent system changes, leveraging machine learning models or predefined rules to pinpoint the root cause of the problem.
+1. The Root Cause Analysis Agent then correlates these anomalies with recent system changes, using machine learning models or predefined rules to pinpoint the root cause of the problem.
 1. Once the root cause is identified, the Automated Deployment Agent takes over to implement fixes or roll back problematic changes by interacting with CI/CD pipelines and executing deployment scripts.
 1. Finally, the Reporting Agent generates detailed reports summarizing the anomalies, root causes, and resolutions, and notifies stakeholders via email or other communication channels.
 
-This modular, scalable, and intelligent multi-agent system streamlines the DevOps process, reducing manual intervention and improving efficiency while ensuring timely communication and resolution of issues.
+This modular, scalable, and intelligent multi-agent system streamlines the DevOps process. The agents collaborate to reduce manual intervention and improve efficiency while ensuring timely communication and resolution of issues.
 
-In this module, we will explore how to leverage the powerful capabilities of Semantic Kernel to design, implement, and orchestrate intelligent agents that work collaboratively to solve complex problems.
-
-In this module, you'll learn how to use the Semantic Kernel Agents Framework to develop your own AI agents that can collaborate for a multi-agent solution.
+In this module, you'll explore how to use the powerful capabilities of Semantic Kernel to design and orchestrate intelligent agents that work collaboratively to solve complex problems. You'll also learn how to use the Semantic Kernel Agents Framework to develop your own AI agents that can collaborate for a multi-agent solution.
 
 After completing this module, you'll be able to:
 

learn-pr/wwl-data-ai/orchestrate-sk-multi-agent-solution/includes/4-define-chat-completion.md

@@ -7,9 +7,9 @@ This unit uses the example multi-agent solution where we have two agents:
 
 ## Termination strategy
 
-A termination strategy ensures that conversations or tasks conclude appropriately, preventing unnecessary messages to the user, limiting resource usage, and improving user experience.
+A termination strategy ensures that conversations or tasks conclude appropriately. This strategy prevents unnecessary messages to the user, limits resource usage, and improves the overall user experience.
 
-For example, once the _ReviewingDirectorAgent_ has reviewed and approved our scrubbing brush slogan from the _CopywriterAgent_, us humans know the conversation should be over. However, if we don't define when to terminate the conversation, the _CopywriterAgent_ is going to keep submitting slogans unnecessarily.
+For example, once the _ReviewingDirectorAgent_ reviews and approves our scrubbing brush slogan from the _CopywriterAgent_, us humans know the conversation should be over. However, if we don't define when to terminate the conversation, the _CopywriterAgent_ is going to keep submitting slogans unnecessarily.
 
 ### Why use a termination strategy?
 
@@ -21,7 +21,7 @@ For example, once the _ReviewingDirectorAgent_ has reviewed and approved our scr
 
 Similar to how the selection strategy is specified, developers can specify a termination strategy or use one of the predefined strategies. Termination strategies can also define a maximum number of iterations a conversation should be limited to.
 
-Termination strategies can be created by a prompt, such as:
+Termination strategies can be created using a prompt, such as:
 
 ```python
 prompt="""
@@ -34,12 +34,12 @@ prompt="""
 
 You can also specify which agent should determine that termination, which in our case would be _ReviewingDirectorAgent_. The agents to determine termination are also defined in the `AgentGroupChat`.
 
-If it makes more sense for a given scenario, developers can also define termination functions explicitly in code. For example, we could define a termination function that checks the most recent history entry for just the word "yes". There are other use-case specific considerations if that route is chosen, such as very explicit agent instructions for approval, but it is an option.
+If it makes more sense for a given scenario, developers can also define termination functions explicitly in code. For example, we could define a termination function that checks the most recent history entry for just the word "yes". There are other use-case specific considerations if that route is chosen, such as explicit agent instructions for approval, but it's an option.
 
 ### Conversation state
 
-Whether you use `AgentGroupChat` for a single-turn or multi-turn conversation, the state is updated to _completed_ once it meets the termination criteria. However, you may want to use the group chat instance again. To keep using the same chat instance, you'll need to reset the completion state to `False`. Without a state reset, the `AgentGroupChat` won't accept new interactions.
+Whether you use `AgentGroupChat` for a single-turn or multi-turn conversation, the state is updated to _completed_ once it meets the termination criteria. However, you may want to use the group chat instance again. To keep using the same chat instance, you'll need to reset the completion state to `False`. Without a state reset, the `AgentGroupChat` can't accept new interactions.
 
-In the case of a conversation hitting the maximum number of iterations allowed, the conversation will end but will not be marked as _completed_. In this case, you can extend the conversation without resetting the conversation state.
+When a conversation hits the maximum number of iterations allowed, the conversation will end but won't be marked as _completed_. In this case, you can extend the conversation without resetting the conversation state.
 
 By understanding these components, you can better utilize the Semantic Kernel Agent Framework to build intelligent multi-agent systems.

learn-pr/wwl-data-ai/orchestrate-sk-multi-agent-solution/includes/5-exercise.md

@@ -1,4 +1,4 @@
-Now it's your opportunity to build a multi agent solution with the Semantic Kernel Agent Framework. In this exercise, you'll create an application that automatically triages and resolves issues presented in log files of a system. Using Azure AI Agents, you'll create an incident manager agent an a devops agent that will collaborate to fix the issues.
+Now it's your opportunity to build a multi agent solution with the Semantic Kernel Agent Framework. In this exercise, you create an application that automatically triages and resolves issues presented in log files of a system. Using Azure AI Agents, you create an incident manager agent and a devops agent that collaborates to fix the issues.
 
 Launch the exercise and follow the instructions.