Microsoft AI Agents: A Deep Dive into Frameworks and Platforms

Microsoft is betting big on AI agents, projecting to have 1.3 billion agents in operation by 2028. These agents are not the average chatbot; they are autonomous entities designed to take action in the enterprise workflows to scientific discovery.

With customer success stories already boasting revenue increases of 142% and negotiation times cut by 70%, the promise of Microsoft AI Agents is rapidly becoming a reality. Let’s examine how Microsoft’s foundational frameworks, builder platforms, and real-world Microsoft AI agents enterprise case studies are driving this AI transformation.

In this article, you’ll read:

What are AI Agents?
The Paradigm Shift: From Tools to Agents
Foundational Pillars: Microsoft AI Agent Frameworks
Accelerating Development: Azure AI Agent Builder Platforms
Conclusion

What are AI Agents?

An AI agent is a software entity engineered to perform tasks autonomously or semi-autonomously by receiving input, processing information, and taking actions to achieve specific goals. It moves beyond traditional AI models, which often function as specific tools for tasks like classification or prediction, by imbuing AI with greater autonomy, flexibility, and interactivity.

This transition from AI models as “tools” to AI as “agents” represents a fundamental shift in how intelligent systems will be leveraged. It signifies a move from task-specific assistance towards more autonomous process ownership and collaborative problem-solving.

Also read: Microsoft Agent 365: The Enterprise Platform for AI Agent Governance

In addition, this evolution necessitates the development of new frameworks for creation, orchestration, and governance. In this area, Microsoft has made substantial investments and is strategically positioning itself as an important enabler.

“Agent development requires a new approach that breaks with traditional deterministic programming. Instead of defining rigid workflows, processes must be broken down into modular tools that the agent can autonomously select based on context and its knowledge. This method grants true decision-making autonomy to the agent, while the human retains a supervisory and control role, thus embodying the “Human in the Loop” principle, where artificial intelligence acts independently under human supervision.”

Mehdi El Yassir, Head of Microsoft Power Platform/CTO, Devoteam

The Paradigm Shift: From Tools to Agents

Foundational Pillars: Microsoft AI Agent Frameworks

Microsoft provides foundational frameworks that enable developers to build and integrate AI agent capabilities into various applications.

These frameworks, Semantic Kernel and AutoGen, offer their own approach and strengths. They cater to different development needs and use case complexities.

Semantic Kernel Agent Framework

The Semantic Kernel Agent Framework offers a platform within the broader Semantic Kernel ecosystem for creating AI agents and embedding agentic patterns into applications. It is designed to enhance modularity and ease of maintenance for both simple and sophisticated agent implementations.

Core Architecture and Components:

Semantic Kernel agents are software entities that operate with varying degrees of autonomy, processing inputs and taking actions via models, tools, and human guidance to achieve specific objectives. The framework’s architecture is built upon the core Semantic Kernel SDK, which is a prerequisite for any agent-related packages. For.NET developers, key NuGet packages include:

Microsoft.SemanticKernel: Contains the core libraries essential for initiating work with the Agent Framework.
Microsoft.SemanticKernel.Agents.Abstractions: Defines the fundamental agent abstractions, typically included via other core or OpenAI agent packages.
Microsoft.SemanticKernel.Agents.Core: Includes the ChatCompletionAgent.
Microsoft.SemanticKernel.Agents.OpenAI: Enables the use of OpenAI Assistant APIs through the OpenAIAssistantAgent.
Microsoft.SemanticKernel.Agents.Orchestration: Provides the orchestration capabilities for the framework. For other programming languages, the semantic-kernel.agents module serves a similar purpose, containing various agent types and orchestration classes.

The design philosophy of Semantic Kernel appears to center on embedding agent-like capabilities within broader applications. This makes it a versatile tool for developers aiming to enhance existing systems with AI intelligence, rather than solely creating standalone agents. This approach lowers the barrier to adopting agentic AI for many organisations that have substantial investments in existing software infrastructure.

Flexibility, Extensibility, and Use Cases:

The Semantic Kernel Agent Framework provides considerable flexibility and extensibility, enabling developers to build adaptive and powerful AI solutions:

Modular Components: Developers can define agent types tailored for specific tasks such as data scraping, API interaction, or natural language processing. This modularity simplifies adaptation as application requirements evolve or new technologies emerge.
Agent Collaboration: Multiple agents can work in concert on complex tasks, creating sophisticated systems with distributed intelligence. For instance, one agent might collect data, another analyse it, and a third make decisions based on the analysis.
Human-Agent Collaboration: The framework supports “human-in-the-loop” interactions, where agents augment human decision-making processes, thereby enhancing productivity.
Process Orchestration: Agents can coordinate tasks across various systems, tools, and APIs, automating end-to-end processes such as application deployments, cloud resource orchestration, or even creative workflows.

The framework is particularly well-suited for applications demanding autonomy and decision-making (e.g., robotic systems, smart environments), multi-agent collaboration (e.g., supply chain management, distributed computing), and interactive, goal-oriented behaviour (e.g., virtual assistants, task planners). The emphasis on modular components and collaboration directly underpins its flexibility and extensibility, allowing for dynamic adaptation to changing needs and technologies.

AutoGen Framework (v0.4)

AutoGen is an open-source framework from Microsoft designed for developing applications with multiple AI agents. It empowers developers and researchers to create intelligent applications leveraging Large Language Models (LLMs), tool integration, and sophisticated multi-agent collaboration patterns. The release of AutoGen v0.4 marks an evolution, reimagining the framework’s foundation for enhanced scale, extensibility, and robustness based on user feedback and research advancements.

Layered Architecture and Core Capabilities:

AutoGen v0.4 brings the adoption of an actor model for multi-agent orchestration, a well-established programming model for concurrent and high-utilisation systems. Implemented in early 2024, the architectural shift to a layered framework enhances modularity, stability, and scalability.

The framework consists of:

AutoGen Core: This foundational layer implements the actor model. It facilitates asynchronous message exchange between agents via a runtime and features event-driven agents that compute in response to these messages. This design decouples message delivery from agent handling, inherently improving modularity and scalability, particularly for deployment scenarios. The event-driven nature also provides mechanisms to observe and control agent behaviour.
AutoGen AgentChat: Built upon AutoGen Core, this layer provides a simplified, user-friendly API optimised for rapid prototyping. It retains the popular pre-built agents (like user proxy agent, assistant agent, and group chat) while incorporating essential features such as streaming support, serialisation, state management, and agent memory.
Extensions: This outermost layer offers advanced runtimes, tools, clients, and integrations with third-party software, continuously expanding the framework’s capabilities.

The shift to an actor model and this structured architecture signifies a move towards industrialising multi-agent systems. Why? Because it addresses critical enterprise requirements for robustness, scalability, and manageability in complex AI deployments.

Scalability, Modularity, and Ecosystem:

Autogen Core’s event-driven architecture offers advantages. It enhances modularity and scalability, and allows for running multiple agents across different processes. It even supports implementing agents in different programming languages. This architecture supports a broad class of multi-agent patterns, including both static and dynamic workflows.

The AutoGen ecosystem is also expanding with tools and applications:

AutoGen Studio: A low-code tool for authoring multi-agent applications, upgraded in v0.4 to include a drag-and-drop multi-agent builder, real-time task updates, flow visualisations, execution controls, and component galleries to foster community collaboration. The development of such tools indicates Microsoft’s intent to lower the entry barrier for creating sophisticated multi-agent applications.
Magentic-One: A team of generalist agents designed for file and web-related tasks, built using AutoGen, is now available within the ecosystem. It offers sophisticated orchestrators and specialised agents. Furthermore, Microsoft Research is collaborating with the Semantic Kernel team to provide an enterprise-ready multi-agent runtime for AutoGen, further solidifying its applicability in demanding environments.

Comparison of Microsoft AI Agent Frameworks (Semantic Kernel vs. AutoGen)

Feature	Semantic Kernel Agent Framework	AutoGen Framework (v0.4)
Primary Goal	Integrate agentic patterns into new/existing applications	Build complex, collaborative multi-agent systems
Architecture Style	SDK with modular components, plugins, and orchestration	Layered architecture (Core, AgentChat, Extensions) with actor model
Agent Collaboration Model	Agents can collaborate; supports human-in-the-loop	Designed for multi-agent conversations and dynamic workflows
Modularity	High; define agents for specific tasks	High; actor model decouples message delivery from handling
Extensibility	High; adaptable as requirements/technologies evolve	High; ecosystem of extensions, tools, and integrations
Development Focus	Embedding AI intelligence into applications	Creating sophisticated multi-agent applications
Use Cases	Augmenting apps with autonomy, process orchestration	Complex problem solving, research, simulations with multiple agents
Low-Code Tooling	Not inherently low-code; relies on SDK usage.	AutoGen Studio for low-code multi-agent authoring

So, what framework is best for you?

Semantic Kernel is optimal for developers looking to infuse existing applications with agent-like intelligence and automate specific processes within a broader software context

AutoGen is tailored for scenarios requiring the development of more autonomous, conversational, and collaborative multi-agent systems designed to tackle complex problems through distributed reasoning and task execution.

Accelerating Development: Azure AI Agent Builder Platforms

Beyond foundational frameworks, Microsoft offers builder platforms designed to streamline and accelerate the development and deployment of AI agents, catering to both professional developers and citizen developers.

Azure AI Foundry and Agent Service

Azure AI Foundry is a platform that amalgamates models, tools, frameworks, and governance capabilities to facilitate the construction and operation of intelligent AI agents. It aims to provide an end-to-end “operating system” for AI agent development, emphasising not only creation but also management, governance, and observability crucial for production environments.

Features and Functionalities:

At the core of this platform is the Azure AI Foundry Agent Service. This service acts as the operational backbone, managing conversation threads, orchestrating tool calls, enforcing content safety policies, and integrating seamlessly with enterprise systems for identity (Microsoft Entra), networking, and observability.

Each agent developed within this service comprises three fundamental components:

Model (LLM) for reasoning and language understanding,
Instructions defining its goals and behaviour
Tools that enable it to retrieve knowledge or execute actions.

Developers gain access to an extensive and continually growing catalogue of over 1,900 LLMs, including models from Azure OpenAI (GPT-4o, GPT-4, GPT-3.5), xAI (Grok 3, Grok 3 mini), Meta (Llama), and others.

Agents can be equipped with various tools to access enterprise knowledge sources (such as Bing, SharePoint, and Azure AI Search) and take real-world actions through integrations with Logic Apps, Azure Functions, and OpenAPI specifications.

Orchestration example

Simplifying Enterprise AI Development and Deployment:

Azure AI Foundry simplifies the journey from AI agent prototype to production through its “Agent Factory” concept. This structured workflow contains:

Model Selection: Choosing an appropriate LLM from the extensive catalogue.
Customisation: Tailoring the model using techniques like fine-tuning, distillation, or domain-specific prompting to align with specific use case requirements.
AI Tool Integration: Equipping the agent with the necessary tools for knowledge retrieval and action execution.
Orchestration: Leveraging the Agent Service to manage the agent’s lifecycle, including tool calls, state updates, retries, and logging.
Trust and Safety: Applying enterprise-grade security and compliance features, including Microsoft Entra ID for identity, Role-Based Access Control (RBAC), content filters, encryption, and network isolation. Options for platform-managed or bring-your-own infrastructure are available.
Observability: Continuously testing and monitoring agents with capabilities to capture logs, traces, and evaluations, offering full thread-level visibility and integration with Application Insights.

By abstracting away underlying infrastructure complexities and embedding trust and safety by design, Azure AI Foundry enables organisations to deploy AI agents with greater confidence and speed. This simplification, coupled with governance, directly addresses common enterprise barriers to AI adoption.

Tools and Integrations:

Azure AI Foundry Models: Provides access to a vast selection of models, facilitating choice and flexibility.
Azure AI Foundry Observability: Offers built-in metrics for monitoring performance, quality, cost, and safety, alongside detailed tracing capabilities for debugging and optimisation.
Model Leaderboard and Model Router: Announced at Microsoft Build 2025, these tools assist developers in comparing AI models and automatically matching the best model to a specific task, enhancing performance and cost-efficiency.
Developer Tooling: Integrates with popular developer environments like GitHub, Visual Studio, and also with Microsoft Copilot Studio, streamlining workflows.
Deployment Flexibility: Supports both cloud and edge deployments, with Foundry Local enabling agent operation in offline or privacy-sensitive environments.

Microsoft Copilot Studio

Microsoft Copilot Studio is a low-code platform designed to empower users to create and customise AI agents and copilots. It plays a pivotal role in Microsoft’s strategy to democratise AI agent creation, extending these capabilities beyond professional developers.

Low-Code Agent Creation and Customisation:

Copilot Studio is a declarative and visual Agent Designer. It features custom data connections, advanced conversation design tools, tasks and autonomous agents, making it accessible for users with varying technical expertise. Key innovations include streamlined Multi-Agent Orchestration, Bring Your Own Model (BYOM) capabilities, and Custom AI Tuning.

For instance, a law firm could create an agent that generates documents aligned with its unique expertise and style. Within Microsoft 365 Copilot, an Agent Builder allows end-users to create agents using natural language instructions and direct integration with sources like SharePoint.

“Copilot Studio combines intuitive drag-and-drop design with enterprise-grade capabilities that deliver immediate ROI. The platform’s 1,800+ connectors create an unmatched integration ecosystem, while its multi-modal RAG and agent orchestration capabilities position organisations at the forefront of AI automation.“

Mehdi El Yassir

Multi-Agent Orchestration and Enterprise Integration:

Copilot Studio has introduced new features for multi-agent orchestration. This enables different agents, potentially built across Microsoft 365, Azure AI, and Microsoft Fabric, to exchange data, collaborate on tasks, and share workloads based on their respective expertise. An example of this could involve HR, IT, and marketing agents collaborating to streamline the new employee onboarding process.

Visual Studio Integration

Copilot Studio is also strengthening its enterprise credentials with improved governance tools, including Data Loss Prevention (DLP) enforcement and deeper integration with Microsoft Entra ID. Publishing channels are expanding beyond traditional web and Teams integrations to include platforms like SharePoint and WhatsApp (coming in July).

The dual focus of Copilot Studio, democratising agent creation for business users while simultaneously enhancing its enterprise features like multi-agent orchestration, aims to drive widespread adoption and extract value from AI agents across all organisational levels. This increasing sophistication begins to blur the lines between “low-code” and “pro-code” agent development, suggesting a future convergence where business users can initiate and manage more complex agent interactions.

Channel publishing

Overview of Azure AI Agent Builder Platforms (Azure AI Foundry vs. Copilot Studio)

Feature	Azure AI Foundry (& Agent Service)	Microsoft Copilot Studio
Platform Focus	Unified enterprise platform for building, deploying, and managing intelligent AI agents	Low-code platform for creating and customising copilots and AI agents
Target Developer	Professional developers, AI engineers	Citizen developers, business users, “makers”
Development Approach	Code-first, highly customizable, full lifecycle management	Low-code, visual designer, natural language instructions (Agent Builder)
Model Access	1,900+ models (Azure OpenAI, Grok, Llama, etc.), Model Leaderboard, Model Router	Access to all exposed AI Foundry models (e.g., GPT-4 via M365 Copilot), Copilot Tuning with own data
Customization	Fine-tuning, distillation, domain-specific prompts, custom tools	Copilot Tuning, custom data connections (+1800 connectors, Microsoft and third-party), workflow design
Orchestration	Agent Service for thread management, tool calls, multi-agent coordination	Multi-agent orchestration capabilities, task delegation between agents
Governance & Security	Entra ID, RBAC, content filters, encryption, network isolation, observability	Inherits capabilities from Power Platform (Entra, DLP, document classification, observability via App Insights, network isolation via Managed Environments…)
Core Strengths	Enterprise-grade scalability, security, lifecycle management, model flexibility	Rapid development, democratisation of AI, deep M365 integration, business user empowerment
Integration Points	GitHub, Visual Studio, Azure services, third-party APIs	Microsoft 365, Power Platform, Azure AI, Microsoft Fabric, SharePoint, Teams, external data sources

To sum it up:

Azure AI Foundry aims at professional development teams requiring deep control and infrastructure for building complex, scalable enterprise AI applications.

Microsoft Copilot Studio is designed to empower a broader range of users, including those with limited coding skills, to rapidly create and customise AI agents, particularly within the Microsoft 365 ecosystem.

The choice between these platforms, or their combined use, will depend on an organisation’s specific project requirements, available technical expertise, desired level of customisation, and integration needs.

Conclusion

Microsoft provides a comprehensive suite of AI agent solutions, from foundational frameworks to robust builder platforms, aiming to create an interconnected “open agentic web.” With a projected 1.3 billion AI agents by 2028, the technology’s pervasiveness is clear. Microsoft’s strong ecosystem integration gives it a distinct advantage. The success of this strategy hangs on creating a developer community, maintaining strong security and ethical practices, and delivering demonstrable ROI.

“Agents can sometimes surpass traditional development approaches; however, traditional methods may be more economically viable in other instances. Therefore, a thorough assessment of business needs and ROI calculation is critical before choosing an agent-based solution. This analysis should encompass both the added value and associated costs, including initial development and ongoing operational expenses.”