wehub-resource-sync
239 lines
8.4 KiB
Plaintext
239 lines
8.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Lesson 11 - Agent-to-Agent (A2A) Protocol"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install agent-framework azure-ai-projects azure-identity python-dotenv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import dotenv\n",
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"from agent_framework import tool, AgentResponseUpdate, WorkflowBuilder\n",
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"from agent_framework.foundry import FoundryChatClient\n",
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"from azure.identity import DefaultAzureCredential\n",
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"\n",
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"dotenv.load_dotenv()\n",
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"\n",
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"endpoint = os.getenv(\"AZURE_AI_PROJECT_ENDPOINT\")\n",
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"deployment_name = os.getenv(\"AZURE_AI_MODEL_DEPLOYMENT_NAME\")\n",
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"\n",
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"missing = [k for k, v in {\n",
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" \"AZURE_AI_PROJECT_ENDPOINT\": endpoint,\n",
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" \"AZURE_AI_MODEL_DEPLOYMENT_NAME\": deployment_name\n",
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"}.items() if not v]\n",
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"\n",
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"if missing:\n",
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" raise ValueError(\n",
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" f\"Missing required environment variables: {', '.join(missing)}. \"\n",
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" \"Please set them as environment variables (e.g., in your .env file or shell environment).\"\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create the Microsoft Foundry client\n",
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"client = FoundryChatClient(\n",
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" project_endpoint=endpoint,\n",
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" model=deployment_name,\n",
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" credential=DefaultAzureCredential()\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## What is the A2A Protocol?\n",
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"\n",
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"The **Agent-to-Agent (A2A) protocol** is an open standard that enables AI agents to communicate,\n",
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"discover each other, and collaborate — even when they are built on different frameworks or hosted\n",
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"by different services.\n",
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"\n",
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"Key concepts:\n",
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"\n",
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"- **Discovery** – Agents publish an *Agent Card* that describes their capabilities, making it\n",
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" easy for other agents (or orchestrators) to find the right specialist for a task.\n",
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"- **Message Passing** – Agents exchange structured messages through a common protocol, so a\n",
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" request from one agent can be understood and fulfilled by another regardless of its internal\n",
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" implementation.\n",
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"- **Task Lifecycle** – A2A defines states such as *submitted*, *working*, *completed*, and\n",
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" *failed*, giving the orchestrator full visibility into how a delegated task is progressing.\n",
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"\n",
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"In this lesson we simulate A2A-style collaboration by wiring three specialized travel agents\n",
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"into a workflow where each agent contributes its expertise and passes results to the next."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Creating Specialized Travel Agents"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"currency_agent = client.as_agent(\n",
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" name=\"CurrencyExchangeAgent\",\n",
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" instructions=\"\"\"You are a currency exchange specialist. You help travelers understand:\n",
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"- Current exchange rates between currencies\n",
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"- Best times to exchange money\n",
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"- Tips for getting the best rates\n",
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"When asked about a destination, provide relevant currency information.\"\"\",\n",
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")\n",
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"\n",
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"activity_agent = client.as_agent(\n",
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" name=\"ActivityPlannerAgent\",\n",
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" instructions=\"\"\"You are a local activities specialist. You recommend:\n",
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"- Must-see attractions and hidden gems\n",
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"- Local experiences and cultural activities\n",
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"- Restaurant and dining recommendations\n",
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"Tailor suggestions to the traveler's interests.\"\"\",\n",
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")\n",
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"\n",
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"travel_manager = client.as_agent(\n",
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" name=\"TravelManagerAgent\",\n",
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" instructions=\"\"\"You are a travel manager who coordinates between specialist agents.\n",
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"When planning a trip:\n",
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"1. Gather currency information from the currency specialist\n",
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"2. Get activity recommendations from the activity planner\n",
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"3. Synthesize everything into a cohesive travel brief\n",
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"Present the final plan in an organized, easy-to-read format.\"\"\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Multi-Agent Collaboration via Workflow\n",
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"\n",
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"We connect the three agents into a sequential workflow that mirrors A2A message passing:\n",
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"\n",
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"1. **CurrencyExchangeAgent** receives the user request and produces currency guidance.\n",
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"2. **ActivityPlannerAgent** receives the enriched context and adds activity recommendations.\n",
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"3. **TravelManagerAgent** synthesizes both inputs into a final travel brief."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"workflow = WorkflowBuilder(start_executor=currency_agent) \\\n",
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" .add_edge(currency_agent, activity_agent) \\\n",
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" .add_edge(activity_agent, travel_manager) \\\n",
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" .build()\n",
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"\n",
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"last_author = None\n",
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"events = workflow.run(\n",
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" \"Plan a week-long trip to Tokyo. I love food, temples, and technology.\",\n",
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" stream=True,\n",
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")\n",
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"async for event in events:\n",
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" if event.type == \"output\" and isinstance(event.data, AgentResponseUpdate):\n",
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" update = event.data\n",
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" author = update.author_name\n",
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" if author != last_author:\n",
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" if last_author is not None:\n",
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" print()\n",
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" print(f\"\\n{'='*50}\")\n",
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" print(f\"🤖 {author}:\")\n",
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" print(f\"{'='*50}\")\n",
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" last_author = author\n",
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" print(update.text, end=\"\", flush=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Understanding A2A in Production\n",
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"\n",
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"In a production environment the A2A protocol unlocks powerful cross-service scenarios:\n",
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"\n",
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"| Capability | Description |\n",
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"|---|---|\n",
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"| **Cross-framework interop** | An agent built with one framework can delegate tasks to an agent built with any other A2A-compliant framework, enabling true cross-organization interoperability. |\n",
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"| **Service boundaries** | Agents can live in separate microservices, cloud regions, or even different organisations while still collaborating seamlessly. |\n",
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"| **Dynamic discovery** | An orchestrator can query an Agent Card registry at runtime to find the best-suited specialist for a given sub-task. |\n",
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"| **Streaming & push notifications** | A2A supports Server-Sent Events (SSE) for real-time progress updates and push notifications for long-running tasks. |\n",
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"\n",
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"The workflow we built above is a simplified, in-process version of this pattern. In a real\n",
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"deployment each agent would expose an HTTP endpoint, publish an Agent Card, and communicate\n",
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"via the A2A JSON-RPC protocol."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Summary\n",
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"\n",
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"In this lesson you learned:\n",
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"\n",
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"1. **What the A2A protocol is** — an open standard for agent-to-agent discovery, messaging,\n",
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" and task management.\n",
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"2. **How to create specialized agents** — a Currency Exchange agent, an Activity Planner agent,\n",
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" and a Travel Manager orchestrator.\n",
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"3. **How to wire agents into a workflow** — using `WorkflowBuilder` to model sequential\n",
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" message passing between agents.\n",
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"4. **How A2A works in production** — enabling cross-framework, cross-service collaboration\n",
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" with dynamic discovery and streaming updates."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.13"
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"nbformat": 4,
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"nbformat_minor": 2
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