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Update Foundry branding (microsoft#6999)
Replace user-facing Azure AI Foundry branding with Microsoft Foundry across docs, samples, comments, and display text while preserving technical identifiers. Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
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‎.github/upgrades/prompts/SemanticKernelToAgentFramework.md‎

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@@ -23,16 +23,16 @@ For each project that needs to be migrated, you need to do the following:
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- Identify the specific Semantic Kernel agent types being used:
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- `ChatCompletionAgent` → `ChatClientAgent`
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- `OpenAIAssistantAgent` → `assistantsClient.CreateAIAgent()` (via OpenAI Assistants client extension)
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- `AzureAIAgent` → `persistentAgentsClient.CreateAIAgent()` (via Azure AI Foundry client extension)
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- `AzureAIAgent` → `persistentAgentsClient.CreateAIAgent()` (via Microsoft Foundry client extension)
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- `OpenAIResponseAgent` → `responsesClient.CreateAIAgent()` (via OpenAI Responses client extension)
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- `A2AAgent` → `AIAgent` (via A2A card resolver)
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- `BedrockAgent` → Custom implementation required (not supported)
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- Determine if agents are being created new or retrieved from hosted services:
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- **New agents**: Use `CreateAIAgent()` methods
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- **Existing hosted agents**: Use `GetAIAgent(agentId)` methods for OpenAI Assistants and Azure AI Foundry
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- **Existing hosted agents**: Use `GetAIAgent(agentId)` methods for OpenAI Assistants and Microsoft Foundry
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</agent_type_identification>
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- Determine the AI provider being used (OpenAI, Azure OpenAI, Azure AI Foundry, etc.)
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- Determine the AI provider being used (OpenAI, Azure OpenAI, Microsoft Foundry, etc.)
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- Analyze tool/function registration patterns
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- Review thread management and invocation patterns
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@@ -90,7 +90,7 @@ below in wrong order or skip any of them):
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you generate report when migration complete. Report should contain:
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- all project dependencies changes (mention what was changed, added or removed, including provider-specific packages)
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- all code files that were changed (mention what was changed in the file, if it was not changed, just mention that the file was not changed)
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- provider-specific migration patterns used (OpenAI, Azure OpenAI, Azure AI Foundry, A2A, ONNX, etc.)
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- provider-specific migration patterns used (OpenAI, Azure OpenAI, Microsoft Foundry, A2A, ONNX, etc.)
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- all cases where you could not convert the code because of unsupported features and you were unable to find a workaround
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- unsupported providers that require custom implementation (Bedrock, CopilotStudio)
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- breaking glass pattern migrations (InnerContent → RawRepresentation) and any CodeInterpreter or advanced tool usage
@@ -223,7 +223,7 @@ using Microsoft.Agents.AI;
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// Provider-specific namespaces (add only if needed):
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using OpenAI; // For OpenAI provider
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using Azure.AI.OpenAI; // For Azure OpenAI provider
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using Azure.AI.Agents.Persistent; // For Azure AI Foundry provider
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using Azure.AI.Agents.Persistent; // For Microsoft Foundry provider
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using Azure.Identity; // For Azure authentication
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```
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</configuration_changes>
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var assistantClient = new OpenAIClient(apiKey).GetAssistantClient();
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await assistantClient.DeleteThreadAsync(thread.ConversationId);
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// For Azure AI Foundry (when cleanup is needed):
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// For Microsoft Foundry (when cleanup is needed):
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var persistentClient = new PersistentAgentsClient(endpoint, credential);
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await persistentClient.Threads.DeleteThreadAsync(thread.ConversationId);
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1. Remove `thread.DeleteAsync()` calls
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2. Use provider-specific client for cleanup when required
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3. Access thread ID via `thread.ConversationId` property
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4. Only implement cleanup for providers that require it (Assistants, Azure AI Foundry)
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4. Only implement cleanup for providers that require it (Assistants, Microsoft Foundry)
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</api_changes>
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### Provider-Specific Creation Patterns
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.CreateAIAgent(instructions: instructions);
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```
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**Azure AI Foundry (New):**
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**Microsoft Foundry (New):**
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```csharp
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AIAgent agent = new PersistentAgentsClient(endpoint, credential)
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.CreateAIAgent(model: deploymentName, instructions: instructions);
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```
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**Azure AI Foundry (Existing):**
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**Microsoft Foundry (Existing):**
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```csharp
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AIAgent agent = await new PersistentAgentsClient(endpoint, credential)
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.GetAIAgentAsync(agentId);
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```
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</api_changes>
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### 4. Azure AI Foundry (AzureAIAgent) Migration
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### 4. Microsoft Foundry (AzureAIAgent) Migration
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<configuration_changes>
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**Remove Semantic Kernel Packages:**

‎.github/workflows/dotnet-build-and-test.yml‎

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AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
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AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
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AZURE_OPENAI_ENDPOINT: ${{ vars.AZURE_OPENAI_ENDPOINT }}
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# Azure AI Foundry
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# Microsoft Foundry
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AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
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AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZURE_AI_MODEL_DEPLOYMENT_NAME }}
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AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}
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AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
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AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
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AZURE_OPENAI_ENDPOINT: ${{ vars.AZURE_OPENAI_ENDPOINT }}
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# Azure AI Foundry
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# Microsoft Foundry
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AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
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AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZURE_AI_MODEL_DEPLOYMENT_NAME }}
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AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}

‎.github/workflows/dotnet-verify-samples.yml‎

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AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
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AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
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AZURE_OPENAI_ENDPOINT: ${{ vars.AZURE_OPENAI_ENDPOINT }}
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# Azure AI Foundry
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# Microsoft Foundry
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AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
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AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZURE_AI_MODEL_DEPLOYMENT_NAME }}
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AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}

‎TRANSPARENCY_FAQ.md‎

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**What is Microsoft Agent Framework?**
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Microsoft Agent Framework is a comprehensive multi-language (C#/.NET and Python) framework for building, orchestrating, and deploying AI agents and multi-agent workflows. The system takes user instructions and conversation inputs and produces intelligent responses through AI agents that can integrate with various LLM providers (OpenAI, Azure OpenAI, Azure AI Foundry). It provides both simple chat agents and complex multi-agent workflows with graph-based orchestration.
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Microsoft Agent Framework is a comprehensive multi-language (C#/.NET and Python) framework for building, orchestrating, and deploying AI agents and multi-agent workflows. The system takes user instructions and conversation inputs and produces intelligent responses through AI agents that can integrate with various LLM providers (OpenAI, Azure OpenAI, Microsoft Foundry). It provides both simple chat agents and complex multi-agent workflows with graph-based orchestration.
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**What can Microsoft Agent Framework do?**
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- **Multi-Agent Orchestration**: Group chat, sequential, concurrent, and handoff patterns
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- **Graph-based Workflows**: Connect agents and deterministic functions using data flows with streaming, checkpointing, time-travel, and Human-in-the-loop
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- **Extensibility Framework**: Extend with native functions, A2A, Model Context Protocol (MCP)
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- **LLM Integration**: Support for OpenAI, Azure OpenAI, Azure AI Foundry, and other providers
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- **LLM Integration**: Support for OpenAI, Azure OpenAI, Microsoft Foundry, and other providers
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- **Runtime Support**: Both in-process and distributed agent execution
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**What is/are Microsoft Agent Framework's intended use(s)?**

‎docs/decisions/0002-agent-tools.md‎

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### AI Agent Tool Types Availability
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Tool Type | Azure AI Foundry Agent Service | OpenAI Assistant API | OpenAI ChatCompletion API | OpenAI Responses API | Amazon Bedrock Agents | Google | Anthropic | Description
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Tool Type | Microsoft Foundry Agent Service | OpenAI Assistant API | OpenAI ChatCompletion API | OpenAI Responses API | Amazon Bedrock Agents | Google | Anthropic | Description
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-- | -- | -- | -- | -- | -- | -- | -- | --
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Function Calling | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | Enables custom, stateless functions to define specific agent behaviors.
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Code Interpreter | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | Allows agents to execute code for tasks like data analysis or problem-solving.
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#### Function Calling
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<details>
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<summary>Azure AI Foundry Agent Service</summary>
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<summary>Microsoft Foundry Agent Service</summary>
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Source: <a href="https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/function-calling?pivots=rest">https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/function-calling?pivots=rest</a>
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Message Request:
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#### Code Interpreter
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<summary>Microsoft Foundry Agent Service</summary>
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<p>Source: <a href="https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/code-interpreter-samples?pivots=rest-api">https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/code-interpreter-samples?pivots=rest-api</a></p>
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<p>.NET Support: ✅</p>
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#### Search and Retrieval
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<summary>Microsoft Foundry Agent Service</summary>
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Source: <a href="https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/file-search-upload-files?pivots=rest">https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/file-search-upload-files?pivots=rest</a>
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File Search Request:
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#### Web Search
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<details>
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<summary>Azure AI Foundry Agent Service</summary>
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<summary>Microsoft Foundry Agent Service</summary>
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Source: <a href="https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/bing-code-samples?pivots=rest">https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/bing-code-samples?pivots=rest</a>
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Bing Search Message Request:
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#### OpenAPI Spec Tool
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<details>
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<summary>Azure AI Foundry Agent Service</summary>
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<summary>Microsoft Foundry Agent Service</summary>
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Source: <a href="https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/openapi-spec-samples?pivots=rest-api">https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/openapi-spec-samples?pivots=rest-api</a><br>
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Source: <a href="https://learn.microsoft.com/en-us/rest/api/aifoundry/aiagents/run-steps/get-run-step?view=rest-aifoundry-aiagents-v1&tabs=HTTP#runstepopenapitoolcall">https://learn.microsoft.com/en-us/rest/api/aifoundry/aiagents/run-steps/get-run-step?view=rest-aifoundry-aiagents-v1&tabs=HTTP#runstepopenapitoolcall</a>
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<details>
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<summary>Azure AI Foundry Agent Service</summary>
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<summary>Microsoft Foundry Agent Service</summary>
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Source: <a href="https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/azure-functions-samples?pivots=rest">https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/azure-functions-samples?pivots=rest</a>
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Message Request:
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#### Microsoft Fabric
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<details>
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<summary>Azure AI Foundry Agent Service</summary>
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<summary>Microsoft Foundry Agent Service</summary>
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Source: <a href="https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/fabric?pivots=rest">https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/fabric?pivots=rest</a>
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Message Request:

‎docs/decisions/0006-userapproval.md‎

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- Also see [the openai human-in-the-loop guide](https://openai.github.io/openai-agents-js/guides/human-in-the-loop/#approval-requests).
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- Also see [the openai MCP guide](https://openai.github.io/openai-agents-js/guides/mcp/#optional-approval-flow).
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- Also see [MCP Approval Requests from OpenAI](https://platform.openai.com/docs/guides/tools-remote-mcp#approvals).
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- Also see [Azure AI Foundry MCP Approvals](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/model-context-protocol-samples?pivots=rest#submit-your-approval).
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- Also see [Microsoft Foundry MCP Approvals](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/model-context-protocol-samples?pivots=rest#submit-your-approval).
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- Also see [MCP Elicitation requests](https://modelcontextprotocol.io/specification/draft/client/elicitation)
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## Decision Drivers

‎docs/decisions/0009-support-long-running-operations.md‎

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### 1. Methods for Working with Long-Running Operations
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Based on the analysis of existing APIs that support long-running operations (such as OpenAI Responses, Azure AI Foundry Agents, and A2A),
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Based on the analysis of existing APIs that support long-running operations (such as OpenAI Responses, Microsoft Foundry Agents, and A2A),
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the following operations are used for working with long-running operations:
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- **Start Long-Running Execution**: Initiates a long-running operation and returns its Id.
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| API | Can Resume Streaming | Model |
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|-------------------------|--------------------------------------|------------------------------------------------------------------------------------------------------------|
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| OpenAI Responses | Yes | StreamingResponseUpdate.**SequenceNumber** + GetResponseStreamingAsync(responseId, **startingAfter**, ct) |
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| Azure AI Foundry Agents | Emulated<sup>2</sup> | RunStep.**Id** + custom pseudo code: client.Runs.GetRunStepsAsync(...).AllStepsAfter(**stepId**) |
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| Microsoft Foundry Agents | Emulated<sup>2</sup> | RunStep.**Id** + custom pseudo code: client.Runs.GetRunStepsAsync(...).AllStepsAfter(**stepId**) |
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| A2A | Implementation dependent<sup>1</sup> | |
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<sup>1</sup> The [A2A specification](https://github.com/a2aproject/A2A/blob/main/docs/topics/streaming-and-async.md#1-streaming-with-server-sent-events-sse)
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allows an A2A agent implementation to decide how to handle streaming resumption: _If a client's SSE connection breaks prematurely while
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a task is still active (and the server hasn't sent a final: true event for that phase), the client can attempt to reconnect to the stream using the tasks/resubscribe RPC method.
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The server's behavior regarding missed events during the disconnection period (e.g., whether it backfills or only sends new updates) is implementation-dependent._
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<sup>2</sup> The Azure AI Foundry Agents API has an API to start a streaming run but does not have an API to resume streaming from a specific point in the stream.
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<sup>2</sup> The Microsoft Foundry Agents API has an API to start a streaming run but does not have an API to resume streaming from a specific point in the stream.
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#### Required Changes
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| resp_2 | 10 | resp.output_item.done | - | InProgress | |
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| resp_2 | 11 | resp.completed | Completed | Completed | |
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Sequence of updates from Azure AI Foundry Agents API to answer the question "What time is it?" using a function call:
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Sequence of updates from Microsoft Foundry Agents API to answer the question "What time is it?" using a function call:
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| Id | SN | UpdateKind | Run.Status | Step.Status | Message.Status | ChatResponseUpdate.Status | Description |
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|--------|---------|-------------------|----------------|-------------|-----------------|---------------------------|---------------------------------------------------|
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| run_1 | - | RunCreated | Queued | - | - | Queued | |
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To support long-running operations, the following values need to be returned by the GetResponseAsync and GetStreamingResponseAsync methods:
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- `ConversationId` - identifier of the conversation or thread the long-running operation is part of. Some APIs, like Azure AI Foundry Agents, use
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- `ConversationId` - identifier of the conversation or thread the long-running operation is part of. Some APIs, like Microsoft Foundry Agents, use
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this identifier together with the ResponseId to identify a run.
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- `SequenceNumber` - identifier of an update within a stream of updates. This is required to support streaming resumption by the GetStreamingResponseAsync method only.
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- `Status` - status of the long-running operation: whether it is queued, running, failed, cancelled, completed, etc.
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In case an agent supports either or both cancellation and deletion of long-running operations, it will override the corresponding methods.
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Some agents, for example Azure AI Foundry Agents, require the thread identifier to cancel a run. To accommodate this requirement, the `CancelRunAsync` method
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Some agents, for example Microsoft Foundry Agents, require the thread identifier to cancel a run. To accommodate this requirement, the `CancelRunAsync` method
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```csharp
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<details>
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<summary>Microsoft Foundry Agents</summary>
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- Create a thread and run the agent against it and wait for it to complete using polling:
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```csharp

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