AI App with both MCP and A2A

 

Service Now AI Agent

Business App ( CMDB )  - will also take care heigene 

ensure A2A protocol working

 Workflow

KB articles as Agents

Tool 

MCP Vs A2A

 

A2A and the Model Context Protocol (MCP) are complementary standards for building robust agentic applications:

• MCP (Model Context Protocol): Connects agents to tools, APIs, and resources with structured inputs/outputs. Think of it as the way agents access their capabilities.
• A2A (Agent2Agent Protocol): Facilitates dynamic, multimodal communication between different agents as peers. It's how agents collaborate, delegate, and manage shared tasks.

langGrpah Open Agent Framework

 

Highlights

• No different from Azurre AI Foundry or AWS bedrock or GCP Agent garden
• Supports only parallel execution of Tasks
• No suppor for Context Memory
• Suports native connectors
• Available in Private cloud

Information Systems Security Agent ( Multi Agent)

Agentic AI-powered security automation framework that performs:

1. Threat modeling (using STRIDE, OWASP Top 10),

2. Risk assessment (using DREAD/FAIR models),

3. Architecture parsing (from diagrams or IaC),

4. Compliance mapping (to NIST, ISO, SOC2),

5. Live Azure infrastructure auditing (e.g., VNet, NSG, Key Vaults, Route Tables),

6. Automated remediation planning (with suggested fixes),

7. Audit-ready reporting (in PDF or dashboard format),

8. ServiceNow integration (for CMDB, tickets, approvals).

Build this using a multi-agent system (LangGraph or Azure AI Foundry Agents) coordinated by a Supervisor Agent, with contextual memory (e.g., Azure AI Search or Weaviate). Include:

• A system architecture diagram,

• A step-by-step description of each agent's function and AI implementation,

• An ROI analysis per agent (quantifying time/money saved),

• A PowerPoint presentation summarizing all components,

• A PNG diagram of the architecture, and

• Exportable formats (PowerPoint, draw.io, or PlantUML if needed).

How to Create Agent in Azure AI Foundry

 https://learn.microsoft.com/en-us/azure/ai-foundry/agents/overview?context=%2Fazure%2Fai-foundry%2Fcontext%2Fcontext 

pip install azure-ai-projects pip install azure-identity

import os from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential from azure.ai.agents.models import CodeInterpreterTool # Create an Azure AI Client from an endpoint, copied from your Azure AI Foundry project. # You need to login to Azure subscription via Azure CLI and set the environment variables project_endpoint = os.environ["PROJECT_ENDPOINT"] # Ensure the PROJECT_ENDPOINT environment variable is set # Create an AIProjectClient instance project_client = AIProjectClient( endpoint=project_endpoint, credential=DefaultAzureCredential(), # Use Azure Default Credential for authentication api_version="latest", ) code_interpreter = CodeInterpreterTool() with project_client: # Create an agent with the Bing Grounding tool agent = project_client.agents.create_agent( model=os.environ["MODEL_DEPLOYMENT_NAME"], # Model deployment name name="my-agent", # Name of the agent instructions="You are a helpful agent", # Instructions for the agent tools=code_interpreter.definitions, # Attach the tool ) print(f"Created agent, ID: {agent.id}") # Create a thread for communication thread = project_client.agents.threads.create() print(f"Created thread, ID: {thread.id}") # Add a message to the thread message = project_client.agents.messages.create( thread_id=thread.id, role="user", # Role of the message sender content="What is the weather in Seattle today?", # Message content ) print(f"Created message, ID: {message['id']}") # Create and process an agent run run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id) print(f"Run finished with status: {run.status}") # Check if the run failed if run.status == "failed": print(f"Run failed: {run.last_error}") # Fetch and log all messages messages = project_client.agents.messages.list(thread_id=thread.id) for message in messages: print(f"Role: {message.role}, Content: {message.content}") # Delete the agent when done project_client.agents.delete_agent(agent.id) print("Deleted agent")