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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesMicrosoft Agent Framework is a developer framework for composing AI agents and explicit workflows. It is not a mandate to make everything autonomous. Microsoft’s own guidance is blunt: “If you can write a function to handle it, do that instead of using an AI agent.” This guide explains how the pieces fit together, how to choose between a function, an agent and a workflow, and what you remain responsible for when you ship.
What the framework is made of
Microsoft’s overview describes four main areas: agents, harness agents, workflows, and integrations. Supporting building blocks include model clients, agent sessions (state), context providers (memory), middleware, and MCP clients for tools.
- Agents: the basic unit that runs a task against a model, optionally calling tools.
- Harness agents: an opinionated set of capabilities for longer, multi-step tasks.
- Workflows: explicit execution paths that coordinate code, agents, state, events and human input.
- Integrations: connections to model and cloud services such as Microsoft Foundry, Anthropic, Azure OpenAI, OpenAI and Ollama, among others.
What an agent actually is
An agent here is more than a prompt sent to a model. According to the agent concepts page, an Agent Framework agent “combines an agent abstraction, a model or remote-agent connection, instructions, tools, middleware, context providers, and session state behind a consistent run interface.”
That composition is the useful part. Because every agent exposes the same run interface, you can swap the model behind it, add middleware, or attach memory without rewriting the code that calls it. A remote agent can sit behind the same interface as one backed by a local model connection.
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Function, agent or workflow?
Microsoft’s overview has a section titled “When to use agents vs workflows”, and the .NET agents concept page gives similar advice. Agents suit open-ended or conversational work that benefits from autonomous tool use and planning. Workflows suit processes where steps and control flow should be explicit. A plain function suits everything that is fixed and deterministic.
| Axis | Ordinary function | Single agent | Workflow |
|---|---|---|---|
| Task shape | Fixed, deterministic | Open-ended, conversational | Defined steps with coordination |
| Who controls execution | Your code | The model chooses tools and plans | Explicit paths, branches and orchestration you define |
| Coordination | One operation | One agent, several tools | Several functions, agents or tools exchanging state and handing off work |
| Human involvement | Whatever you build | Tool approval (harness capability) | Workflows can coordinate human input |
A practical rule: start with the cheapest option that works. If a function handles it, stop there. If the input is unpredictable and the model must decide which tools to use, use an agent. If you find yourself wanting guaranteed ordering, branching, or several agents cooperating, move to a workflow.
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Build up in stages
Microsoft’s getting-started sequence follows a natural progression. Follow the same order in your own project and stop when the task is solved.
1. A minimal agent
Create an agent with a model connection and instructions, then call its run interface. Use this to confirm credentials, model access and basic behavior before adding anything else. Package names and install commands change between releases and languages, so take them from the current language-specific guide in the documentation hub rather than from older blog posts.
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2. Add a function tool
An agent can call tools, including functions you write and tools exposed by MCP servers, as part of a run. Keep tools narrow and well described, since the model decides when to call them.
3. Keep a multi-turn session
An agent session holds conversation state across turns. Treat this as conversation state, not as your application’s data design: what you store, for how long and where is still your decision.
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4. Add memory and persistence
Context providers supply memory to the agent. Persistence of sessions and memory is a separate concern from the run itself, so plan storage deliberately.
5. Compose a workflow
When the process has defined steps, branching, coordination or human input, use a workflow. The workflow concepts page states that workflows “define explicit, inspectable execution paths for coordinating code, agents, state, events, and human input.” Graph-based workflow APIs are supported across the SDKs; the Python functional workflow API is identified as experimental. The .NET concepts page names sequential, concurrent, handoff, group-chat and Magentic orchestration patterns. Check the current API-specific guide before relying on any particular recovery or durability behavior; the concepts page does not promise it.
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6. Use a harness agent for long tasks
For longer, multi-step work, Microsoft lists harness capabilities: planning and todo tracking, context compaction, file access and memory, tool approval, and observability. These are framework capabilities, not a guarantee that your application is safe or reliable by default. You still have to configure approvals and test the result.
7. Host it
The final step in the getting-started path is hosting the agent or workflow as a service. Follow the hosting guide for your language.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Language and preview caveats
Microsoft identifies Go as public preview in the getting-started material, and notes that declarative agents, RAG, CodeAct and functional workflows are not yet available in Go. That caveat is specific to Go; do not assume it applies to other languages. Integration availability and behavior are also version-sensitive, so check the current integration guide for your provider. Microsoft’s pages were last updated in August 2026, which does not certify the package version you will install.
Your responsibilities before production
The overview is clear that developers remain responsible for:
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- Reviewing third-party data practices, terms and costs for any model or service you connect.
- Controlling data flows across compliance and geographic boundaries.
- Testing the specific application, not just the framework.
- Implementing suitable safety mitigations.
Microsoft’s pages are conceptual and do not publish performance, cost or reliability figures, so measure latency, token usage and failure rates in your own environment.
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