Update Blog “llm-agentic-tool-mesh-orchestrating-agentic-tools-for-the-next-revolution”

Author: antfin

content/blog/llm-agentic-tool-mesh-orchestrating-agentic-tools-for-the-next-revolution.md

@@ -1,5 +1,5 @@
 ---
-title: "LLM Agentic Tool Mesh: Orchestrating agentic tools for the next revolution"
+title: "LLM Agentic Tool Mesh: Orchestrating agentic tools for the AI revolution"
 date: 2025-01-20T08:36:14.226Z
 author: Antonio Fin
 authorimage: /img/afin_photo.jpg
@@ -17,15 +17,15 @@ li {
 }
 </style>
 
-In our previous blog posts, we delved into the [Chat Service](https://developer.hpe.com/blog/ll-mesh-exploring-chat-service-and-factory-design-pattern/), [Agent Service](https://developer.hpe.com/blog/llm-agentic-tool-mesh-harnessing-agent-services-and-multi-agent-ai-for-next-level-gen-ai/), and [RAG Service](https://developer.hpe.com/blog/llm-agentic-tool-mesh-empowering-gen-ai-with-retrieval-augmented-generation-rag/) of [LLM Agentic Tool Mesh open source project](https://github.com/HewlettPackard/llmesh). Today, we'll explore the System Services of LLM Agentic Tool Mesh that are essential for managing and orchestrating the mesh of agentic tools. 
+In my previous blog posts, I dolve into the [Chat Service](https://developer.hpe.com/blog/ll-mesh-exploring-chat-service-and-factory-design-pattern/), [Agent Service](https://developer.hpe.com/blog/llm-agentic-tool-mesh-harnessing-agent-services-and-multi-agent-ai-for-next-level-gen-ai/), and [RAG Service](https://developer.hpe.com/blog/llm-agentic-tool-mesh-empowering-gen-ai-with-retrieval-augmented-generation-rag/) of [LLM Agentic Tool Mesh open source project](https://github.com/HewlettPackard/llmesh). Today, I'll explore the system services of LLM Agentic Tool Mesh, which are essential for managing and orchestrating the mesh of agentic tools. 
 
-We'll provide insights into these services, showcase an example of a Mesh available in the repository, discuss federated governance, and share our vision for the future evolution of the LLM Agentic Tool Mesh project.
+I'll provide insights into these services, showcase an example of a Mesh available in the repository, discuss federated governance, and share our vision for the future evolution of the LLM Agentic Tool Mesh project.
 
 ![](/img/mesh.png)
 
-# Understanding the System Services
+# Understanding the system services
 
-The System Services in LLM Agentic Tool Mesh are crucial for the seamless operation and orchestration of agentic tools and web applications. These services ensure consistency, ease of use, and flexibility across the platform. They include:
+The system services in LLM Agentic Tool Mesh are crucial for the seamless operation and orchestration of agentic tools and web applications. These services ensure consistency, ease of use, and flexibility across the platform. They include:
 
 1. **Tool Client Service**
 2. **Tool Server Service**
@@ -38,8 +38,8 @@ The Tool Client Service enables developers to transform any code function into a
 
 Key features:
 
-* Decorator-based: Convert functions into tools using the `@AthonTool` decorator.
-* Seamless integration: Decorated functions are fully integrated into the LLM Agentic Tool Mesh platform.
+* Decorator-based: Convert functions into tools using the `@AthonTool` decorator
+* Seamless integration: Decorated functions are fully integrated into the LLM Agentic Tool Mesh platform
 
 Example usage:
 
@@ -64,8 +64,8 @@ The Tool Server Service provides the necessary infrastructure to manage and run
 
 Key features:
 
-* Tool discovery: Automatically discover tools within the platform.
-* Execution management: Manage the execution of tools, ensuring efficient operation.
+* Tool discovery: Automatically discover tools within the platform
+* Execution management: Manage the execution of tools, ensuring efficient operation
 
 ![](/img/tools.png)
 
@@ -118,20 +118,20 @@ for tool in tool_repository.get_tools().tools:
 
 # Building a mesh of LLM Agentic Tools
 
-We have developed a series of web applications and tools, complete with examples, to demonstrate the capabilities of LLM Agentic Tool Mesh in out [GitHub repo](https://github.com/HewlettPackard/llmesh).
+We have developed a series of web applications and tools, complete with examples, to demonstrate the capabilities of LLM Agentic Tool Mesh in our [GitHub repo](https://github.com/HewlettPackard/llmesh).
 
-Web Applications:
+Web applications:
 
-* **Chatbot** (`examples/app_chatbot`): A chatbot capable of reasoning and invoking appropriate LLM tools to perform specific actions. You can configure the chatbot using files that define LLM Agentic Tool Mesh platform services, project settings, toolkits, and memory configurations. The web app orchestrates both local and remote LLM tools, allowing them to define their own HTML interfaces, supporting text, images, and code presentations.
-* **Admin panel** (`examples/app_backpanel`): An admin panel that enables the configuration of basic LLM tools to perform actions via LLM calls. It allows you to set the system prompt, select the LLM model, and define the LLM tool interface, simplifying the process of configuring LLM tool interfaces.
+* **Chatbot** (`examples/app_chatbot`): This is a chatbot capable of reasoning and invoking appropriate LLM tools to perform specific actions. You can configure the chatbot using files that define LLM Agentic Tool Mesh platform services, project settings, toolkits, and memory configurations. The web app orchestrates both local and remote LLM tools, allowing them to define their own HTML interfaces, supporting text, images, and code presentations.
+* **Admin panel** (`examples/app_backpanel`): There's an admin panel that enables the configuration of basic LLM tools to perform actions via LLM calls. It allows you to set the system prompt, select the LLM model, and define the LLM tool interface, simplifying the process of configuring LLM tool interfaces.
 
 Tools:
 
-* **Basic copywriter** (`examples/tool_copywriter`): A tool that rewrites text, providing explanations for enhancements and changes.
-* **Temperature finder** (`examples/tool_api`): Fetches and displays the current temperature for a specified location by utilizing a public API.
-* **Temperature analyzer** (`examples/tool_analyzer`): Generates code using a language model to analyze historical temperature data and create visual charts for better understanding
-* **Telco expert** (`examples/tool_rag`): A RAG tool that provides quick and accurate access to 5G specifications.
-* **OpenAPI manager** (`examples/tool_agents`): A multi-agent tool that reads OpenAPI documentation and provides users with relevant information based on their queries.
+* **Basic copywriter** (`examples/tool_copywriter`): This tool rewrites text, providing explanations for enhancements and changes.
+* **Temperature finder** (`examples/tool_api`): This tool fetches and displays the current temperature for a specified location by utilizing a public API.
+* **Temperature analyzer** (`examples/tool_analyzer`): Another tools generates code, using a language model to analyze historical temperature data and create visual charts for better understanding
+* **Telco expert** (`examples/tool_rag`): The RAG tool provides quick and accurate access to 5G specifications.
+* **OpenAPI manager** (`examples/tool_agents`): This multi-agent tool reads OpenAPI documentation and provides users with relevant information based on their queries.
 
 ## Running the examples
 
@@ -148,11 +148,11 @@ In the LLM Agentic Tool Mesh platform, the management of LLM tools is decentrali
 
 Key principles:
 
-* **Interoperable standards**: Ensures all tools and services work together seamlessly while adhering to best practices.
-* **Ethical compliance**: Emphasizes minimizing biases, ensuring fairness, and upholding ethical principles across all AI tools and models.
+* **Interoperable standards**: Ensures all tools and services work together seamlessly while adhering to best practices
+* **Ethical compliance**: Emphasizes minimizing biases, ensuring fairness, and upholding ethical principles across all AI tools and models
 * **Security and privacy**: Maintains rigorous standards to protect data and ensure compliance with privacy regulations.
-* **Continuous improvement**: Encourages feedback and collaboration to refine governance practices.
-* **Automated governance**: Plans to extend code quality checks to enforce governance policies, ensuring comprehensive compliance across the platform.
+* **Continuous improvement**: Encourages feedback and collaboration to refine governance practices
+* **Automated governance**: Plans to extend code quality checks to enforce governance policies, ensuring comprehensive compliance across the platform
 
 LLM Agentic Tool Mesh includes a dedicated repository containing text files that outline various policies and standards (`federated_governance/`). These documents cover essential areas such as LLM model usage, RAG processes, and more.
 
@@ -162,26 +162,28 @@ The LLM Agentic Tool Mesh platform is evolving into a comprehensive solution, pr
 
 When creating a new web app, you can build upon the existing examples. With all services fully parameterized, there is unparalleled flexibility to design diverse user experience panels. For instance, current examples include a chatbot as a user interface and an admin panel for configuring an LLM tool. Additionally, web apps can be developed to support deployment tasks or facilitate experiments aimed at optimizing service parameters for specific objectives.
 
-Currently, the platform provides:
+Currently, the platform provides a user panel and a dvelopment panel.
 
 **User panel**:
 
 * Implemented features: This panel focuses on engaging with tools like Chat, RAG, and Agent services. It provides a user-friendly interface for interacting with these capabilities.
-* Future goals: Enrich the existing services, offering an even more seamless and feature-rich experience for end-users.
+* Future goals: We plan to enrich the existing services, offering an even more seamless and feature-rich experience for end-users.
 
 **Development panel**:
 
 * Implemented features: This panel has been partially tackled with the backpanel web app example, which allows users to runtime modify the basic copywriter agentic tool and the RAG tool.
-* Future goals: Add more system services to support development, including real-time LLM tuning and configuration.
+* Future goals: We aim to add more system services to support development, including real-time LLM tuning and configuration.
+
+In the future, we hope to be able to offer a deployment panel and an experiment panel.
 
 **Deployment panel (future)**:
 
 * Purpose: This panel will focus on deploying the LLM Agentic Tool Mesh tools seamlessly across one or more clusters, enabling large-scale and distributed deployments.
-* Planned features: Tools for monitoring deployed tools, orchestrating distributed systems, and managing deployment pipelines.
+* Planned features: We hope to offer tools for monitoring deployed tools, orchestrating distributed systems, and managing deployment pipelines.
 
 **Experiment Panel (Future)**:
 
-* Purpose: Designed for tracking and managing experiments to optimize LLM tool performance and suitability.
+* Purpose: This panel will be designed to track and manage experiments to optimize LLM tool performance and suitability.
 * Planned features: This panel will allow users to try different configurations and compare outcomes, helping teams evaluate the most effective settings for their use cases.
 
 ![](/img/usage.png)