What’s Microsoft Agent Framework? [5 Minutes Overview]

Synthetic intelligence is altering shortly from easy chatbots to extra succesful autonomous brokers that exhibit reasoning, coordination, and execution of complicated duties. Microsoft has lately made Agent Framework publicly out there in public preview as an open-source SDK and runtime to ease the orchestration of multi-agent methods, an vital step ahead for enterprises adopting agentic AI to alleviate fragmentation in tooling whereas now offering a bridge between experimenting and manufacturing.-grade deployment.

What’s Microsoft Agent Framework?

The Microsoft Agent Framework solves a key developer dilemma: selecting between cutting-edge AI analysis and secure, production-ready instruments. It unifies two frameworks:

• AutoGen: Brings superior multi-agent orchestration, permitting AIs to work collectively in complicated methods (e.g., group chats, debates).
• Semantic Kernel: Supplies the enterprise spine, together with safety, sort security, and telemetry.

This merger creates a novel platform the place you’ll be able to construct a working AI agent in below 20 traces of code with out sacrificing the power to create complicated, multi-agent workflows for business use.

Microsoft Agent Framework Structure

The core structure of this framework is comprised of 4 foundational components:

Open Requirements and Interoperability

The Microsoft Agent Framework is constructed on a precept of open requirements and interoperability, guaranteeing brokers can talk throughout totally different platforms and combine seamlessly into current enterprise methods. It helps rising protocols to facilitate collaboration and straightforward device integration.

Key Options

• Cross-Platform Communication: Brokers can discuss to one another throughout totally different runtimes utilizing Agent-to-Agent (A2A) protocols.
• Open Requirements: Helps MCP for real-time device connections and OpenAPI for easy REST API integration.
• Native Connectors: Consists of built-in assist for key providers like Azure AI Foundry, Microsoft Graph, SharePoint, Elasticsearch, and Redis.
• Structure Agnostic: Designed to work with Azure providers, third-party APIs, and customized inner methods with out vendor lock-in.

This strategy permits builders to plug AI brokers instantly into their present know-how stack, bridging the hole between revolutionary AI and established enterprise structure.

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Analysis-to-Manufacturing Pipeline

The framework supplies a robust research-to-production pipeline, combining AutoGen’s superior orchestration patterns with the reliability required for enterprise use. This permits builders to handle complicated, multi-step enterprise processes by means of a structured and stateful workflow layer, which is important for prolonged operations.

This makes the framework superb for remodeling complicated enterprise processes into automated, multi-agent workflows.

Extensibility by Design

Microsoft Agent Framework affords a modular structure that helps agent configuration through the use of each declarative and programmatic kinds. Builders could outline brokers in YAML or JSON format so current versioning and collaborative growth workflows make use of novel DevOps practices in defining brokers. Declaring agent definitions permits groups to handle agent definitions in model management alongside utility code inside GitHub or Azure DevOps repositories. 

Pluggable reminiscence modules additionally enable a developer to retailer context and recall info by means of a number of back-end shops. Whether or not builders use in-memory storage for prototypes, Redis for eventualities with distributed brokers, or some type of proprietary vector database for semantic search, the framework works to supply context no matter structure.

Manufacturing-Prepared from Day One

The framework is engineered for enterprise adoption, integrating important production-grade capabilities for observability, safety, and lifecycle administration instantly into its core.

Key Manufacturing Options:

• Native Observability: Constructed-in OpenTelemetry integration supplies full visibility into agent workflows, device utilization, and inter-agent collaboration, which is important for debugging, efficiency optimization, and compliance auditing.
• Enterprise-Grade Safety: Leverages Azure Entra ID for sturdy authentication and authorization, guaranteeing all brokers function inside strict organizational safety insurance policies.
• Streamlined DevOps: Helps CI/CD pipelines by means of GitHub Actions and Azure DevOps, enabling groups to use a standardized software program growth lifecycle to their AI brokers.

This built-in concentrate on governance and operational excellence ensures that multi-agent methods could be trusted, managed, and scaled successfully inside a real-world enterprise surroundings.

Getting Began with Agent Framework

For Python builders, set up is easy:

pip set up agent-framework --pre

For .NET builders:

dotnet add bundle Microsoft.Brokers.AI

Constructing Your First Agent

Let’s study the right way to create a purposeful agent that may work together with instruments. Right here’s a Python instance that demonstrates the framework’s simplicity:

import asyncio from agent_framework.azure import AzureOpenAIResponsesClient from azure.id import AzureCliCredential # Outline a customized device operate def calculate_discount(value: float, discount_percent: float) -> float:     """Calculate discounted value"""     return value * (1 - discount_percent / 100) async def predominant():     # Initialize agent with Azure OpenAI     agent = AzureOpenAIResponsesClient(         credential=AzureCliCredential()     ).create_agent(         title="ShoppingAssistant",         directions="You assist prospects calculate costs and reductions.",         instruments=[calculate_discount]  # Register the device     )          # Agent can now use the device routinely     response = await agent.run(         "If a laptop computer prices $1200 and has a 15% low cost, what is the ultimate value?"     )     print(response) asyncio.run(predominant())

The equal .NET implementation showcases related class:

utilizing Azure.AI.OpenAI; utilizing Azure.Identification; utilizing Microsoft.Brokers.AI; // Outline a device as a technique static double CalculateDiscount(double value, double discountPercent) {     return value * (1 - discountPercent / 100); } var agent = new AzureOpenAIClient(     new Uri(Surroundings.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!),     new AzureCliCredential())     .GetOpenAIResponseClient("gpt-4")     .CreateAIAgent(         title: "ShoppingAssistant",         directions: "You assist prospects calculate costs and reductions.",         instruments: [CalculateDiscount]); Console.WriteLine(await agent.RunAsync(     "If a laptop computer prices $1200 and has a 15% low cost, what is the ultimate value?"));

Multi-Agent Workflow Instance

For extra complicated eventualities, the framework helps orchestrating a number of specialised brokers. Right here’s a workflow that coordinates analysis and writing brokers:

from agent_framework.workflows import Workflow, WorkflowStep from agent_framework.azure import AzureOpenAIResponsesClient # Create specialised brokers researcher = shopper.create_agent(     title="Researcher",     directions="You analysis subjects and supply factual info." ) author = shopper.create_agent(     title="Author",     directions="You write partaking articles based mostly on analysis." ) # Outline workflow workflow = Workflow(     steps=[         WorkflowStep(             name="research",             agent=researcher,             output_variable="research_data"         ),         WorkflowStep(             name="write",             agent=writer,             input_from="research_data",             output_variable="article"         )     ] ) # Execute workflow outcome = await workflow.run(     input_data={"matter": "Way forward for Quantum Computing"} ) print(outcome["article"])

This workflow illustrates how the framework manages state amongst brokers, passing the researcher’s output as context to the author routinely. An inherent checkpoint system manages elapsed time to make sure the workflow can resume if something fails with out restarting and shedding what was beforehand achieved.

Enterprise Adoption of Microsoft Agent Framework

A number of main organizations are already utilizing the Microsoft Agent Framework in real-world eventualities. Listed here are just a few examples:

• KPMG: Powering KPMG Clara AI, the framework connects specialised brokers to enterprise information and instruments with built-in security safeguards. Open-source connectors allow entry past Azure AI Foundry, supporting scalable multi-agent collaboration in globally regulated environments.
• Commerzbank: Exploring avatar-driven buyer assist to ship pure, accessible, and regulation-compliant interactions.
• Citrix: Evaluating integration into digital desktop infrastructure to reinforce enterprise productiveness.
• Sitecore: Growing agent capabilities for entrepreneurs to automate workflows throughout the content material provide chain.

Voice Integration and Multi-Modal Capabilities

The Voice Stay API is now typically out there. It affords a unified, real-time speech-to-speech interface that mixes:

• Speech-to-text
• Generative AI fashions
• Textual content-to-speech
• Avatars
• Dialog enhancers

This low-latency stream helps voice-initiated and voice-concluded multi-agent workflows, making a extra pure consumer expertise.

Organizations utilizing Voice Stay API embrace:

• Capgemini: Customer support brokers
• healow: Studying tutors
• Astra Tech: HR assistants

These examples spotlight how the framework helps multi-modal agent experiences, extending past text-based interactions.

Addressing Enterprise Issues

Governance and Accountable AI

As AI adoption will increase, enterprises are putting larger emphasis on accountable and compliant use of clever brokers. Based on McKinsey’s 2025 International AI Belief Survey, the greatest barrier to AI adoption is the absence of efficient governance and risk-management instruments.

Key Capabilities

• Brokers stay targeted on their assigned goals and keep away from drifting into unintended duties or behaviors. This ensures operational consistency and reliability.
• These instruments defend in opposition to immediate injection assaults and flag uncontrolled or dangerous agent actions for organizational overview. This improves safety and oversight.
• Mechanically detect when brokers entry Personally Identifiable Data (PII). This enables organizations to evaluate and refine their information dealing with insurance policies based mostly on entry patterns.
• All governance options are constructed into Azure AI Foundry. This supplies a ready-to-use compliance layer that aligns with organizational insurance policies and regulatory requirements.
• These capabilities are important in sectors akin to finance and healthcare, the place accountable AI use should be embedded all through the event and deployment lifecycle.

Developer Expertise: Staying in Circulate

An trade examine reveals that 50% of builders lose over ten hours per week resulting from fragmented instruments and inefficient workflows. This productiveness drain impacts supply timelines and developer morale. The Microsoft Agent Framework addresses this problem by providing a unified growth expertise that minimizes context switching and streamlines agent creation, testing, and deployment.

Key Advantages:

• Builders not have to toggle between terminals, logs, and dashboards. DevUI centralizes these duties, serving to groups keep targeted and productive. 
• Builders can work regionally utilizing the AI Toolkit extension in Visible Studio Code, after which deploy to Azure AI Foundry with observability and compliance options enabled when wanted.
• The framework helps each Python and .NET, permitting groups to work of their most popular language whereas sustaining portability and consistency throughout environments.
• With standardized APIs, builders can collaborate throughout groups and languages without having to be taught new interfaces, which improves effectivity and reduces onboarding time.
• The brand new DevUI supplies an interactive interface that helps agent growth, testing, and debugging. It enhances code-first workflows and simplifies fast prototyping and troubleshooting.

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Conclusion

The Microsoft Agent Framework is shaping the way forward for enterprise AI by merging innovation with governance, multi-modal capabilities, and developer-first tooling. It transforms experimentation into scalable, compliant options. As clever brokers grow to be central to enterprise workflows, this framework affords a dependable basis.

What are your ideas on adopting agentic AI in your group utilizing this framework? Let me know within the remark part under!




Knowledge Science Trainee at Analytics Vidhya
I’m at present working as a Knowledge Science Trainee at Analytics Vidhya, the place I concentrate on constructing data-driven options and making use of AI/ML strategies to resolve real-world enterprise issues. My work permits me to discover superior analytics, machine studying, and AI functions that empower organizations to make smarter, evidence-based choices.
With a robust basis in pc science, software program growth, and information analytics, I’m captivated with leveraging AI to create impactful, scalable options that bridge the hole between know-how and enterprise.
📩 You may as well attain out to me at [email protected]