Microsoft Agent Framework

Intelligent AI agents for your .NET applications with Microsoft Agent Framework

Microsoft's AI agent framework enables you to build autonomous agents that plan, reason, and execute complex workflows — integrating seamlessly with Azure AI, semantic kernel, and your existing .NET services.

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Key highlights

Why Microsoft Agent Framework is redefining AI application development.

Autonomous agent orchestration

Build agents that plan, reason, and execute multi-step workflows autonomously. Agents can call APIs, query databases, send emails, and coordinate with other agents to complete complex business processes.

Deep .NET integration

Built for the .NET ecosystem. Agents integrate with Semantic Kernel, Azure OpenAI, Entity Framework, and your existing service layer. No need to stitch together disparate AI tools.

Azure-native security

Agents run within your Azure tenant with managed identities, RBAC, and enterprise-grade compliance. All AI calls are governed by your existing Azure policies and content safety filters.

What agents can do for your business

From customer support to data analysis — agents handle the complexity.

Customer support agents that resolve issues end-to-end.

An agent can triage a support ticket, look up the customer's account, check order status, initiate a refund, and send a confirmation email — all autonomously. When it needs human judgment, it escalates with full context.

Data analysis agents that find insights automatically.

Describe what you need in natural language — "find the top 10 customers by revenue this quarter" — and an agent writes the SQL query, runs it against your database, analyzes the results, and produces a formatted report or chart.

Workflow automation that adapts to changing conditions.

Unlike rigid automation scripts, agents adapt. If an order is delayed, the agent can proactively notify the customer, offer alternatives, and update inventory — adjusting its plan based on real-time information.

Observability and human-in-the-loop.

Every agent action is logged. You can review decisions, intervene at any step, and provide feedback. The agent learns from corrections, becoming more accurate over time without requiring retraining.

Why we recommend Microsoft Agent Framework

The best foundation for building autonomous AI agents in .NET.

Microsoft Agent Framework represents a new paradigm for building AI-powered automation. Unlike simple LLM API calls that generate text, agents built with this framework can plan, reason, execute multi-step workflows, and adapt to changing conditions — all while integrating deeply with your existing .NET infrastructure.

We recommend the Agent Framework when you need autonomous, goal-oriented AI that goes beyond chat completions. An agent can be tasked with "resolve this customer support ticket" and autonomously look up the customer's account, check order status, initiate a refund, and send a confirmation email — orchestrating multiple tools and decisions without step-by-step instructions.

The framework's deep integration with the .NET ecosystem sets it apart from alternatives. Agents work naturally with Semantic Kernel for AI orchestration, Entity Framework for database access, Azure OpenAI for language models, and your existing service layer. There's no impedance mismatch between your agent code and the rest of your .NET application — they share types, DI containers, and infrastructure.

For enterprise teams, the security model is a key differentiator. Agents run within your Azure tenant with managed identities, RBAC, and Azure Policy enforcement. Every agent action is logged for audit, human-in-the-loop review is built in, and you can enforce content safety filters on all AI interactions. This is enterprise-grade AI, not a research prototype.

Where Microsoft Agent Framework fits in the stack

Understanding the architectural role of AI agents in your application.

Customer service automation layer

Deploy agents as the first line of customer support. They triage tickets, look up account information, process returns, and answer FAQ queries. When they need human intervention, they escalate with full conversation context and suggested actions.

Data analysis and reporting agents

Connect agents to your databases via natural language. Users ask "what were our top 10 products last quarter?" and the agent writes SQL queries, runs analyses, and produces formatted reports. No BI team bottleneck required.

Workflow orchestration layer

Replace rigid automation scripts with adaptive agents that respond to changing conditions. An order processing agent can handle inventory checks, payment verification, shipping coordination, and exception handling — adjusting its plan based on real-time information.

Alongside existing .NET services

Agents integrate naturally into your existing .NET architecture. They share your DI container, use your EF Core DbContext, call your existing service methods, and respect your transaction boundaries. No separate agent infrastructure — they're just another .NET service.

How to choose the right Agent Framework for the job

Guidance on when to use the Agent Framework — and when a simpler approach works.

Use the Agent Framework when your task requires multiple steps, tool calls, and autonomous decision-making. Direct LLM API calls are fine for simple Q&A, text generation, or single-turn transformations. If you need the AI to plan a sequence of actions, call multiple APIs, handle errors, and adapt its approach — that's when you need an agent framework. For simple use cases like "summarize this text," a direct API call is simpler and cheaper.

Autonomy matters when the steps to complete a task aren't known in advance. An agent that processes customer support tickets doesn't know what tools it will need until it reads the ticket and looks up the account. If your workflow can be expressed as a fixed script or decision tree, you don't need an agent. But if the path depends on the data encountered along the way, agent autonomy is exactly what you need.

An agent framework becomes essential when you're orchestrating multiple tools, handling errors, and managing state across a complex workflow. Without a framework, you end up writing spaghetti code to chain LLM calls, parse responses, call tools, handle failures, and loop back. Microsoft Agent Framework provides structured patterns for planning, execution, error recovery, and human-in-the-loop review — saving you from building this infrastructure yourself.

While Agent Framework works best with Azure OpenAI and Azure infrastructure for enterprise features, it can use any OpenAI-compatible API endpoint. You can run agents with Anthropic's Claude, OpenAI directly, or even self-hosted models via Tornado LLM. However, you'll lose Azure's managed identity integration, content safety filters, and audit logging. For production enterprise use, Azure provides significant security and compliance advantages. For prototyping or non-critical workloads, any LLM provider works.

When to choose Microsoft Agent Framework

A decision framework for project leaders.

Ideal for

Automating complex, multi-step business workflows
Building AI-powered customer support and service agents
Data analysis and reporting with natural language queries
.NET shops wanting deep integration with existing services
Enterprise applications needing Azure compliance and security

Less suited for

Simple automation that a script or scheduled job handles
Teams without existing Azure infrastructure
Applications needing fully offline AI capabilities
Early prototypes where a direct LLM API call suffices

Ready to build your first AI agent?

Let's discuss how Microsoft Agent Framework can automate your most complex workflows.

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