Single and Multi-Agentic Design Patterns Using Azure AI Foundry

Introduction

Agentic design patterns are a set of architectural and design principles that emphasize the autonomous behavior of software agents. These patterns enable the creation of intelligent systems that can perceive, reason, and act independently within their environment. By leveraging Azure AI Foundry, developers can harness the power of Azure's AI and machine learning capabilities to implement these patterns effectively.

This article aims to provide a comprehensive guide for AI users on how to use Azure AI Foundry to implement agentic design patterns. It will cover the key concepts, sample code in Python, and Semantic Kernel orchestration from a Microsoft AI architect's perspective.

Understanding Agentic Design Patterns

Agentic design patterns revolve around the concept of autonomous agents. These agents are software entities that can perceive their environment, make decisions based on their perceptions, and execute actions to achieve specific goals. The core principles of agentic design patterns include:

• Perception: Agents must have the ability to perceive their environment through sensors or data inputs.
• Reasoning: Agents must be capable of processing the perceived information and making decisions based on predefined rules or learned knowledge.
• Action: Agents must be able to execute actions that influence their environment to achieve their goals.
• Autonomy: Agents operate independently without constant human intervention.

Leveraging Azure AI Foundry

Azure AI Foundry is a robust platform that provides a suite of tools and services for building, deploying, and managing AI solutions. It offers capabilities such as machine learning, cognitive services, and bot services, which are essential for implementing agentic design patterns.

Setting Up the Environment

To get started with Azure AI Foundry, you'll need to set up your environment:

# Install the Azure AI Foundry SDK

pip install azure-ai-foundry

# Import the necessary libraries

from azure.ai.foundry import FoundryClient

from azure.identity import DefaultAzureCredential

# Authenticate and create a Foundry client

credential = DefaultAzureCredential()

client = FoundryClient(credential=credential)

Implementing Perception

Perception involves collecting data from the environment. Azure Cognitive Services provide a range of APIs for tasks such as computer vision, speech recognition, and text analysis. Here is an example of using the Computer Vision API to analyze an image:

from azure.cognitiveservices.vision.computervision import ComputerVisionClient

from msrest.authentication import CognitiveServicesCredentials

# Create a Computer Vision client

vision_client = ComputerVisionClient(endpoint, CognitiveServicesCredentials(api_key))

# Analyze an image

image_url = "[URL]"

analysis = vision_client.analyze_image(image_url, visual_features=["Description", "Tags"])

# Print the analysis results

print("Description:", analysis.description.captions[0].text)

print("Tags:", ", ".join([tag.name for tag in analysis.tags]))

Implementing Reasoning

Reasoning involves processing the perceived information and making decisions. This can be achieved using machine learning models. Azure Machine Learning provides a comprehensive platform for training and deploying models. Here is an example of deploying a pre-trained model:

from azureml.core import Workspace

from azureml.core.model import Model

from azureml.core.webservice import AciWebservice, Webservice

# Load the workspace

ws = Workspace.from_config()

# Register and deploy the model

model = Model.register(workspace=ws, model_path="model.pkl", model_name="my_model")

aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

service = Model.deploy(workspace=ws, name="myservice", models=[model], deployment_config=aci_config)

# Wait for the deployment to complete

service.wait_for_deployment(show_output=True)

# Test the deployed model

input_data = {"data": [[1.0, 2.0, 3.0, 4.0]]}

prediction = service.run(input_data)

print("Prediction:", prediction)

Implementing Action

Action involves executing tasks based on the decisions made by the agent. Azure Bot Service allows you to create bots that can interact with users and perform actions. Here is an example of creating a simple bot using the Bot Framework SDK:

from botbuilder.core import BotFrameworkAdapter, TurnContext

from botbuilder.schema import Activity

# Create a Bot Framework adapter

adapter = BotFrameworkAdapter()

# Define the bot logic

async def my_bot_logic(turn_context: TurnContext):

if turn_context.activity.type == "message":

await turn_context.send_activity(Activity(type="message", text="Hello, I am your agent!"))

# Handle incoming requests

async def handle_request(request):

await adapter.process_activity(request, "", my_bot_logic)

Semantic Kernel Orchestration

Semantic Kernel is a powerful tool for orchestrating AI components in a cohesive manner. It allows for the integration of various AI services and ensures that they work together seamlessly. From a Microsoft AI architect's perspective, Semantic Kernel orchestration involves defining workflows, managing dependencies, and optimizing performance.

Defining Workflows

Workflows define the sequence of steps that the agent must follow to achieve its goals. Here is an example of defining a simple workflow using Semantic Kernel:

from semantic_kernel import Kernel, Step

# Define the steps

step1 = Step("Perceive", lambda: print("Perceiving the environment"))

step2 = Step("Reason", lambda: print("Reasoning based on perception"))

step3 = Step("Act", lambda: print("Executing action"))

# Create a kernel and add the steps

kernel = Kernel()

kernel.add_step(step1)

kernel.add_step(step2)

kernel.add_step(step3)

# Execute the workflow

kernel.execute()

Managing Dependencies

Dependencies ensure that the components of the agentic system work together without conflicts. Azure AI Foundry provides tools for managing dependencies, such as dependency injection and service discovery. Here is an example of using dependency injection:

from azure.ai.foundry import DependencyInjection

# Define services

class PerceptionService:

def perceive(self):

print("Perceiving the environment")

class ReasoningService:

def reason(self):

print("Reasoning based on perception")

class ActionService:

def act(self):

print("Executing action")

# Register services

di = DependencyInjection()

di.register(PerceptionService)

di.register(ReasoningService)

di.register(ActionService)

# Resolve and use services

perception_service = di.resolve(PerceptionService)

reasoning_service = di.resolve(ReasoningService)

action_service = di.resolve(ActionService)

perception_service.perceive()

reasoning_service.reason()

action_service.act()

Optimizing Performance

Performance optimization ensures that the agentic system operates efficiently. Azure AI Foundry provides tools for monitoring and optimizing performance, such as Azure Monitor and Application Insights. Here is an example of using Azure Monitor to track performance metrics:

from azure.monitor.query import MetricsQueryClient

from azure.identity import DefaultAzureCredential

# Create a Metrics Query client

client = MetricsQueryClient(credential=DefaultAzureCredential())

# Query metrics

response = client.query(

resource_id="resource_id",

metrics=["Percentage CPU", "Available Memory"],

timespan="PT1H",

interval="PT1M"

)

# Print metrics

for metric in response.metrics:

print(f"Metric: {metric.name}")

for time_series in metric.timeseries:

for data in time_series.data:

print(f"Timestamp: {data.timestamp}, Value: {data.average}")

Conclusion

Agentic design patterns offer a powerful approach to building intelligent systems that can operate autonomously. By leveraging Azure AI Foundry, developers can implement these patterns effectively and create robust, scalable, and efficient AI solutions. This article has provided an overview of the key concepts, sample code in Python, and Semantic Kernel orchestration from a Microsoft AI architect's perspective. With these tools and techniques, you can harness the power of agentic design patterns to build the next generation of intelligent systems.