Further edits

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/agent-output.md

@@ -1,5 +1,5 @@
 ---
-title: Understand and test the AI Agent
+title: Understand and Test the AI Agent
 weight: 5
 
 ### FIXED, DO NOT MODIFY
@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## AI Agent Function Calls
 
-An AI agent, powered by a Large Language Model (LLM), decides which function to use by analyzing the prompt or input it receives, identifying the relevant intent or task, and then matching the intent to the most appropriate function from a pre-defined set of available functions based on its understanding of the language and context.
+An AI agent, powered by a LLM, selects the most appropriate function by analyzing the prompt or input it receives, identifying the relevant intent or task, and then matching the intent to the most appropriate function from a pre-defined set of available functions based on its understanding of the language and context.
 
 Have a look at how this is implemented in the python script `agent.py`:
 

content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/ai-agent-backend.md

@@ -7,7 +7,7 @@ layout: learningpathall
 ---
 
 ## Python Script for executing an AI Agent application
-With `llama.cpp` built and the Llama3.1 8B model downloaded, you are now ready to create a Python script to execute an AI agent Application:
+With `llama.cpp` built and the Llama3.1 8B model downloaded, you are now ready to create a Python script to execute an AI agent application:
 
 Create a Python file named `agent.py` with the content shown below:
 ```bash
@@ -156,5 +156,5 @@ def run_web_search_agent():
 if __name__ == '__main__':
     run_web_search_agent()
 ```
-In the next section, you will inspect this script to understand how the LLM is configured and used to execute Agent tasks using this script. You will then proceed to executing and testing the AI agent.
+In the next section, you will inspect this script to understand how the LLM is configured and used to execute agent tasks. You will then proceed to executing and testing the AI agent.
 

content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/ai-agent.md

@@ -8,18 +8,18 @@ layout: learningpathall
 
 ## Overview of AI Agents
 
-An AI agent as an integrated system that extends beyond basic text generation by augmenting Large Language Models (LLMs) with tools and domain knowledge. 
+An AI agent is an integrated system that extends beyond basic text generation by augmenting Large Language Models (LLMs) with tools and domain knowledge. 
 
 Here’s a closer look at the underlying elements:
 
-- **System**: Each AI agent functions as an interconnected ecosystem of components. Below is a list of key factors and components that affect performance:   
+- **System**: Each AI agent functions as an interconnected ecosystem of components. Below is a list of key factors and components that affect system performance:   
   - **Environment**: The domain in which the AI Agent operates. For instance, in a system that books travel itineraries, this might include airline reservation systems and hotel booking tools.  
   - **Sensors**: The methods the AI agent uses to observe its environment. For a travel agent, these might be APIs that inform the agent about seat availability on flights or room occupancy in hotels.  
   - **Actuators**: Ways the AI agent exerts influence within the environment. In the example of a travel agent, placing a booking or modifying an existing reservation illustrates how actuators function to enact changes within the environment.  
 
 - **Large Language Models**: While agents have long existed, LLMs enhance these systems with powerful language comprehension and data-processing capabilities.  
 - **Action Execution**: Rather than just produce text, LLMs within an agent context interpret user instructions and interact with tools to achieve specific objectives.  
-- **Tools**: The agent’s available toolkit depends on the software environment and developer-defined boundaries. In the travel agent example, these tools might be limited to flight and hotel reservation APIs.  
+- **Tools**: The agent’s available toolkit depends on the software environment and developer-defined boundaries. In the travel agent example in this Learning Path, these tools might be limited to flight and hotel reservation APIs.  
 - **Knowledge**: Beyond immediate data sources, the agent can fetch additional details - perhaps from databases or web services - for enhanced decision making.
 
 
@@ -36,7 +36,7 @@ AI Agents come in multiple forms. The table below provides an overview of some a
 | **Utility-Based Agents** | Use scoring or numerical metrics to compare and select actions that fulfill a goal. | Balances cost versus convenience when determining which flights or hotels to book.                                                      |
 | **Learning Agents**      | Adapt over time by integrating lessons from previous feedback or experiences.        | Adjusts future booking suggestions based on traveler satisfaction surveys.                                                              |
 | **Hierarchical Agents**  | Split tasks into sub-tasks and delegate smaller pieces of work to subordinate agents.| Cancels a trip by breaking down the process into individual steps, such as canceling a flight, a hotel, and a car rental.               |
-| **Multi-Agent Systems**  | Involve multiple agents that may cooperate or compete to complete tasks.             | Cooperative: Different agents each manage flights, accommodations, and excursions. Competitive: Several agents vie for limited rooms.   |
+| **Multi-Agent Systems**  | Involve multiple agents that might cooperate or compete to complete tasks.             | Cooperative: Different agents each manage flights, accommodations, and excursions. Competitive: Several agents vie for limited rooms.   |
 
 
 ## Ideal Applications for AI Agents

content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/set-up.md

@@ -8,11 +8,13 @@ layout: learningpathall
 
 ## Before you begin
 
-This Learning Path demonstrates how to build an AI Agent Application using open-source Large Language Models (LLMs) optimized for Arm architecture. The AI Agent can use Large Language Models (LLMs) to perform actions by accessing tools and knowledge. The instructions in this Learning Path have been designed for Arm servers running Ubuntu 22.04 LTS. You need an Arm server instance with at least 4 cores and 16GB of memory to run this example. Configure disk storage up to at least 32 GB. The instructions have been tested on an AWS EC2 Graviton3 `m7g.xlarge` instance.
+This Learning Path demonstrates how to build an AI Agent Application using open-source LLMs optimized for Arm architecture. The AI Agent can use Large Language Models (LLMs) to perform actions by accessing tools and knowledge. 
+
+The instructions in this Learning Path have been designed for Arm servers running Ubuntu 22.04 LTS. You require an Arm server instance with at least 4 cores and 16GB of memory to run this example. Configure disk storage up to at least 32 GB. The instructions have been tested on an AWS EC2 Graviton3 `m7g.xlarge` instance.
 
 ## Overview
 
-In this Learning Path, you learn how to build an AI Agent application using `llama-cpp-python` and `llama-cpp-agent`. `llama-cpp-python` is a Python binding for `llama.cpp` that enables efficient LLM inference on Arm CPUs and `llama-cpp-agent` provides an interface for processing text using agentic chains with tools.
+In this Learning Path, you will learn how to build an AI agent application using `llama-cpp-python`, and `llama-cpp-agent`. `llama-cpp-python` is a Python binding for `llama.cpp` that enables efficient LLM inference on Arm CPUs and `llama-cpp-agent` provides an interface for processing text using agentic chains with tools.
 
 ## Install Dependencies
 
@@ -43,7 +45,7 @@ pip install llama-cpp-agent pydantic
 ```
 
 
-## Download the pre-quantized Llama-3.1-8B LLM model from Hugging Face
+## Download the Pre-quantized Llama-3.1-8B LLM Model from Hugging Face
 
 You are now ready to download the LLM.
 
@@ -58,7 +60,7 @@ Install the `huggingface_hub` Python library using `pip`:
 ```bash
 pip install huggingface_hub
 ```
-You can now download the [pre-quantized Llama3.1 8B model](https://huggingface.co/cognitivecomputations/dolphin-2.9.4-llama3.1-8b-gguf) using the huggingface cli:
+You can now download the [pre-quantized Llama3.1 8B model](https://huggingface.co/cognitivecomputations/dolphin-2.9.4-llama3.1-8b-gguf) using the `huggingface-cli`:
 
 ```bash
 huggingface-cli download cognitivecomputations/dolphin-2.9.4-llama3.1-8b-gguf dolphin-2.9.4-llama3.1-8b-Q4_0.gguf --local-dir . --local-dir-use-symlinks False