Add intro to agents doc

.openpublishing.redirection.ai.json

@@ -9,10 +9,6 @@
             "redirect_url": "/dotnet/ai/microsoft-extensions-ai",
             "redirect_document_id": true
         },
-        {
-            "source_path_from_root": "/docs/ai/conceptual/agents.md",
-            "redirect_url": "/dotnet/ai"
-        },
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             "source_path_from_root": "/docs/ai/conceptual/evaluation-libraries.md",
             "redirect_url": "/dotnet/ai/evaluation/libraries",

docs/ai/conceptual/agents.md

@@ -0,0 +1,96 @@
+---
+title: Agents
+description: Introduction to agents
+author: luisquintanilla
+ms.author: luquinta
+ms.date: 10/01/2025
+ms.topic: concept-article
+---
+
+# Agents
+
+This article introduces the core concepts behind agents, why they matter, and how they fit into workflows, setting you up to get started building agents in .NET.
+
+## What are agents?
+
+**Agents are systems that accomplish objectives.**
+
+![Components of an agent](../media/agents/agent-components.png)
+
+Agents become more capable when equipped with the following:
+
+- **Reasoning and decision-making**: Powered by LLMs, search algorithms, or planning and decision-making systems.
+- **Tool usage**: Access to Model Context Protocol (MCP) servers, code execution, and external APIs.
+- **Context awareness**: Informed by chat history, threads, vector stores, enterprise data, or knowledge graphs.
+
+These capabilities allow agents to operate more autonomously, adaptively, and intelligently.
+
+## What are workflows?
+
+As objectives grow in complexity, they need to be broken down into manageable steps. That's where workflows come in.
+
+**Workflows define the sequence of steps required to achieve an objective.**
+
+Imagine you're launching a new feature on your business website. If it's a simple update, you might go from idea to production in a few hours. But for more complex initiatives, the process might include:
+
+- Requirement gathering
+- Design and architecture
+- Implementation
+- Testing
+- Deployment
+
+A few important observations:
+
+- Each step might contain subtasks.
+- Different specialists might own different phases.
+- Progress isn’t always linear. Bugs found during testing might send you back to implementation.
+- Success depends on planning, orchestration, and communication across stakeholders.
+
+### Agents + workflows = agentic workflows
+
+Workflows don't require agents, but agents can supercharge them.
+
+When agents are equipped with reasoning, tools, and context, they can optimize workflows.
+
+This is the foundation of multi-agent systems, where agents collaborate within workflows to achieve complex goals.
+
+### Workflow orchestration
+
+Agentic workflows can be orchestrated in a variety of ways. The following are a few of the most common:
+
+- [Sequential](#sequential)
+- [Concurrent](#concurrent)
+- [Handoff](#handoff)
+- [Group chat](#group-chat)
+
+#### Sequential
+
+Agents process tasks one after another, passing results forward.
+
+![Sequential agent orchestration: Task Input → Agent A → Agent B → Agent C → Final Output](../media/agents/sequential-workflow.png)
+
+#### Concurrent
+
+Agents work in parallel, each handling different aspects of the task.
+
+![Concurrent agent orchestration: Task Input → Agents A, B, C → Aggregate Results → Final Output](../media/agents/concurrent-workflow.png)
+
+#### Handoff
+
+Responsibility shifts from one agent to another based on conditions or outcomes.
+
+![Handoff orchestration: Task Input → Agent A Decision → Agent B or Agent A → Agent B Decision → Agent C or Agent B → Final Output](../media/agents/handoff-workflow.png)
+
+#### Group chat
+
+Agents collaborate in a shared conversation, exchanging insights in real-time.
+
+![Group chat orchestration: User and Agents A, B, C collaborate via GroupChat to produce final output](../media/agents/groupchat-workflow.png)
+
+## How can I get started building agents in .NET?
+
+The building blocks in <xref:Microsoft.Extensions.AI> and <xref:Microsoft.Extensions.VectorData> supply the foundations for agents by providing modular components for AI models, tools, and data.
+
+These components serve as the foundation for Microsoft Agent Framework.
+
+For more information, see [Microsoft Agent Framework](/agent-framework/overview/agent-framework-overview).

docs/ai/index.yml

@@ -27,6 +27,8 @@ landingContent:
             url: overview.md
           - text: Microsoft.Extensions.AI libraries
             url: microsoft-extensions-ai.md
+          - text: Microsoft Agent Framework
+            url: /agent-framework/overview/agent-framework-overview?toc=/dotnet/ai/toc.json&bc=/dotnet/ai/toc.json
       - linkListType: get-started
         links:
           - text: Connect to and prompt an AI model

docs/ai/toc.yml

@@ -10,9 +10,9 @@ items:
   - name: Overview
     href: dotnet-ai-ecosystem.md
   - name: Microsoft.Extensions.AI
-    href: microsoft-extensions-ai.md
-  - name: Semantic Kernel
-    href: semantic-kernel-dotnet-overview.md
+    href: microsoft-extensions-ai.md    
+  - name: Microsoft Agent Framework
+    href: /agent-framework/overview/agent-framework-overview?toc=/dotnet/ai/toc.json&bc=/dotnet/ai/toc.json
   - name: C# SDK for MCP
     href: get-started-mcp.md
 - name: Quickstarts
@@ -41,6 +41,8 @@ items:
   items:
   - name: How generative AI and LLMs work
     href: conceptual/how-genai-and-llms-work.md
+  - name: Building agents in .NET
+    href: conceptual/agents.md
   - name: Tokens
     href: conceptual/understanding-tokens.md
   - name: Embeddings