Merge pull request #51 from jun216tee/main

 I corrected a few potential misspellings.

04-tool-use/README.md

@@ -36,7 +36,7 @@ AI Agents can leverage tools to complete complex tasks, retrieve information, or
 
 ## What are the elements/building blocks needed to implement the tool use design pattern?
 
-These building blocks allow the AI agent to perform a wide range of task. Let's look at the key elements needed to implement the Tool Use Design Pattern:
+These building blocks allow the AI agent to perform a wide range of tasks. Let's look at the key elements needed to implement the Tool Use Design Pattern:
 
 - **Function/Tool Calling**: This is the primary way to enable LLMs to interact with tools. Functions or tools are blocks of reusable code that agents use to carry out tasks. These can range from simple functions like a calculator to API calls to third-party services such as stock price lookups or weather forecasts1.
 
@@ -50,7 +50,7 @@ These building blocks allow the AI agent to perform a wide range of task. Let's
 
 - **Content Generation and Editing: Agents can leverage tools like grammar checkers, text summarizers, or content safety evaluators to assist with content creation tasks**.
 
-Next, let's look Function/Tool Calling in more detail.
+Next, let's look at Function/Tool Calling in more detail.
 
 ### Function/Tool Calling
 
@@ -80,7 +80,7 @@ Let's use the example of getting the current time in a city to illustrate:
 1. **Create a Function Schema**:
 
     Next we will define a JSON schema that contains the function name, description of what the function does, and the names and descriptions of the function parameters.
-    We will then take this schema and pass it to the client created previously, along with the users request to find the time in San Francisco. Whats important to note is that a **tool call** is what is returned, **not** the final answer to the question. As mentioned earlier, the LLM returns the name of the function it selected for the task, and the arguments that will be passed to it. 
+    We will then take this schema and pass it to the client created previously, along with the users request to find the time in San Francisco. What's important to note is that a **tool call** is what is returned, **not** the final answer to the question. As mentioned earlier, the LLM returns the name of the function it selected for the task, and the arguments that will be passed to it. 
 
     ```python
     # Function description for the model to read

06-building-trustworthy-agents/README.md

@@ -151,7 +151,7 @@ To build trustworthy AI agents, it is important to understand and mitigate the r
 
 ## Human-in-the-Loop
 
-Another effective way to build trustworthy AI Agent systems is using a Human-in-the-loop. This creates a flow where users are able to provide feedback to the Agents during run. Users essentially act as agent in a multi-agent system and by providing approval or termination of the running process.
+Another effective way to build trustworthy AI Agent systems is using a Human-in-the-loop. This creates a flow where users are able to provide feedback to the Agents during the run. Users essentially act as agents in a multi-agent system and by providing approval or termination of the running process.
 
 ![Human in The Loop](./images/human-in-the-loop.png)
 

07-planning-design/README.md

@@ -196,8 +196,8 @@ For a more comprehensive planning do checkout Magnetic One <a href="https://www.
 
 ## Summary
 
-In this article we have looked at an example of how we can create a planner that can dynamically select the available agents defined. The output of the Planner decomposes the tasks and assigns the agents so them to be executed. It is assumed the agents has access to function/tools that are required to perform the task. In addition to the agents you can include other patterns like reflection, summarizer, and round robin chat to further customize.
+In this article we have looked at an example of how we can create a planner that can dynamically select the available agents defined. The output of the Planner decomposes the tasks and assigns the agents so them to be executed. It is assumed the agents have access to the functions/tools that are required to perform the task. In addition to the agents you can include other patterns like reflection, summarizer, and round robin chat to further customize.
 
 ## Additional Resources
 
-* AutoGen Magentic One - A Generalist multi agent system for solving complex task and has achieved impressive results on multiple challenging agentic benchmarks. Reference: <a href="https://github.com/microsoft/autogen/tree/main/python/packages/autogen-magentic-one" target="_blank">autogen-magentic-one</a>. In this implementation the orchestrator create task specific plan and delegates these tasks to the available agents. In addition to planning the orchestrator also employs a tracking mechanism to monitor the progress of the task and re-plans as required.
+* AutoGen Magentic One - A Generalist multi agent system for solving complex tasks and has achieved impressive results on multiple challenging agentic benchmarks. Reference: <a href="https://github.com/microsoft/autogen/tree/main/python/packages/autogen-magentic-one" target="_blank">autogen-magentic-one</a>. In this implementation the orchestrator create task specific plan and delegates these tasks to the available agents. In addition to planning the orchestrator also employs a tracking mechanism to monitor the progress of the task and re-plans as required.

README.md

@@ -122,4 +122,4 @@ This project may contain trademarks or logos for projects, products, or services
 trademarks or logos is subject to and must follow
 [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/legal/intellectualproperty/trademarks/usage/general).
 Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
-Any use of third-party trademarks or logos are subject to those third-party's policies.
+Any use of third-party trademarks or logos is subject to those third-parties' policies.