Roger Barreto (rogerbarreto) · Sep 24, 2026 · Sep 29, 2026 · Oct 2, 2026 · Oct 2, 2026 · Oct 2, 2026
@@ -109,6 +109,8 @@ jobs:
           FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
           FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
           AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}
+          # TypeSafe (Jev)
+          TYPESAFE_API_KEY: ${{ secrets.TYPESAFE_API_KEY }}
 
       - name: Write Job Summary
         if: always()

@@ -36,6 +36,8 @@
     <Project Path="samples/02-agents/AgentProviders/onnx/Agent_With_ONNX/Agent_With_ONNX.csproj" />
     <Project Path="samples/02-agents/AgentProviders/openai/Agent_With_OpenAIChatCompletion/Agent_With_OpenAIChatCompletion.csproj" />
     <Project Path="samples/02-agents/AgentProviders/openai/Agent_With_OpenAIResponses/Agent_With_OpenAIResponses.csproj" />
+    <Project Path="samples/02-agents/AgentProviders/typesafe/Agent_TypeSafe_Step01_LoopJudge/Agent_TypeSafe_Step01_LoopJudge.csproj" />
+    <Project Path="samples/02-agents/AgentProviders/typesafe/Agent_With_TypeSafe/Agent_With_TypeSafe.csproj" />
   </Folder>
   <Folder Name="/Samples/05-end-to-end/DevUIAspireIntegration/">
     <Project Path="samples/05-end-to-end/DevUIAspireIntegration/DevUIIntegration.AppHost/DevUIIntegration.AppHost.csproj" />
@@ -69,6 +71,7 @@
     <Project Path="samples/02-agents/Agents/Agent_Step22_AgentMode/Agent_Step22_AgentMode.csproj" />
     <Project Path="samples/02-agents/Agents/Agent_Step23_TodoList/Agent_Step23_TodoList.csproj" />
     <Project Path="samples/02-agents/Agents/Agent_Step24_MultiModelRouting/Agent_Step24_MultiModelRouting.csproj" />
+    <Project Path="samples/02-agents/Agents/Agent_Step25_ClassificationWithJev/Agent_Step25_ClassificationWithJev.csproj" />
   </Folder>
   <Folder Name="/Samples/02-agents/DeclarativeAgents/">
     <Project Path="samples/02-agents/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />
@@ -631,6 +634,7 @@
     <Project Path="src/Microsoft.Agents.AI.OpenAI/Microsoft.Agents.AI.OpenAI.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Purview/Microsoft.Agents.AI.Purview.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Tools.Shell/Microsoft.Agents.AI.Tools.Shell.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.TypeSafe/Microsoft.Agents.AI.TypeSafe.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Valkey/Microsoft.Agents.AI.Valkey.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.Foundry/Microsoft.Agents.AI.Workflows.Declarative.Foundry.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.Mcp/Microsoft.Agents.AI.Workflows.Declarative.Mcp.csproj" />
@@ -689,6 +693,7 @@
     <Project Path="tests/Microsoft.Agents.AI.OpenAI.UnitTests/Microsoft.Agents.AI.OpenAI.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Purview.UnitTests/Microsoft.Agents.AI.Purview.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Tools.Shell.UnitTests/Microsoft.Agents.AI.Tools.Shell.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.TypeSafe.UnitTests/Microsoft.Agents.AI.TypeSafe.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.UnitTests/Microsoft.Agents.AI.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Valkey.UnitTests/Microsoft.Agents.AI.Valkey.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.Mcp.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.Mcp.UnitTests.csproj" />

@@ -29,6 +29,7 @@
       "src\\Microsoft.Agents.AI.OpenAI\\Microsoft.Agents.AI.OpenAI.csproj",
       "src\\Microsoft.Agents.AI.Purview\\Microsoft.Agents.AI.Purview.csproj",
       "src\\Microsoft.Agents.AI.Tools.Shell\\Microsoft.Agents.AI.Tools.Shell.csproj",
+      "src\\Microsoft.Agents.AI.TypeSafe\\Microsoft.Agents.AI.TypeSafe.csproj",
       "src\\Microsoft.Agents.AI.Valkey\\Microsoft.Agents.AI.Valkey.csproj",
       "src\\Microsoft.Agents.AI.Workflows.Declarative.Foundry\\Microsoft.Agents.AI.Workflows.Declarative.Foundry.csproj",
       "src\\Microsoft.Agents.AI.Workflows.Declarative.Mcp\\Microsoft.Agents.AI.Workflows.Declarative.Mcp.csproj",
@@ -64,6 +65,7 @@
       "tests\\Microsoft.Agents.AI.OpenAI.UnitTests\\Microsoft.Agents.AI.OpenAI.UnitTests.csproj",
       "tests\\Microsoft.Agents.AI.Purview.UnitTests\\Microsoft.Agents.AI.Purview.UnitTests.csproj",
       "tests\\Microsoft.Agents.AI.Tools.Shell.UnitTests\\Microsoft.Agents.AI.Tools.Shell.UnitTests.csproj",
+      "tests\\Microsoft.Agents.AI.TypeSafe.UnitTests\\Microsoft.Agents.AI.TypeSafe.UnitTests.csproj",
       "tests\\Microsoft.Agents.AI.UnitTests\\Microsoft.Agents.AI.UnitTests.csproj",
       "tests\\Microsoft.Agents.AI.Valkey.UnitTests\\Microsoft.Agents.AI.Valkey.UnitTests.csproj",
       "tests\\Microsoft.Agents.AI.Workflows.Declarative.Mcp.UnitTests\\Microsoft.Agents.AI.Workflows.Declarative.Mcp.UnitTests.csproj",

@@ -77,6 +77,49 @@ internal static class AgentsSamples
             ],
         },
 
+        new SampleDefinition
+        {
+            Name = "Agent_With_TypeSafe",
+            ProjectPath = "samples/02-agents/AgentProviders/typesafe/Agent_With_TypeSafe",
+            RequiredEnvironmentVariables = ["TYPESAFE_API_KEY"],
+            Inputs = ["Y"],
+            MustContain =
+            [
+                "Direct JevChatClient result:",
+                "Agent result:",
+                "Default questions result:",
+                "Function calling result:",
+                "better_city:",
+                "Tool approval:",
+                "issue_refund(plan: Pro)",
+            ],
+            ExpectedOutputDescription =
+            [
+                "The direct result should route the duplicate subscription charge to billing, and the agent result should route the failing checkout integration to technical with a high urgency probability.",
+                "The default questions result should route the invoice request to billing with a low urgency probability.",
+                "The function calling result should contain one weather line for Seattle and one for Amsterdam, with temperatures in Fahrenheit, followed by a better_city line that names one of the two cities.",
+                "The tool approval step should ask to approve issue_refund for the Pro plan and, after approval, show the refund result followed by a department line.",
+                "The output should not contain error messages or stack traces.",
+            ],
+        },
+
+        new SampleDefinition
+        {
+            Name = "Agent_TypeSafe_Step01_LoopJudge",
+            ProjectPath = "samples/02-agents/AgentProviders/typesafe/Agent_TypeSafe_Step01_LoopJudge",
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT", "TYPESAFE_API_KEY"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
+            MustContain = ["Answer 1:", "Judge:", "red_sunsets:", "Final answer:"],
+            ExpectedOutputDescription =
+            [
+                "The first answer should explain only why the sky is blue, and the judge should mark red_sunsets as missing with a low probability.",
+                "A later answer should also explain why sunsets are red, and the judge should mark red_sunsets as met for it.",
+                "Every numbered answer (Answer 1, Answer 2, and so on) should be followed by a Judge block with probabilities. The Final answer section repeats the last judged answer and has no Judge block of its own.",
+                "The final answer should explain both why the sky is blue and why sunsets are red.",
+                "The output should not contain error messages or stack traces.",
+            ],
+        },
+
         // ── Agents ──────────────────────────────────────────────────────────
 
         new SampleDefinition
@@ -403,6 +446,29 @@ internal static class AgentsSamples
             ],
         },
 
+        new SampleDefinition
+        {
+            Name = "Agent_Step25_ClassificationWithJev",
+            ProjectPath = "samples/02-agents/Agents/Agent_Step25_ClassificationWithJev",
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT", "TYPESAFE_API_KEY"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
+            MustContain =
+            [
+                "Ticket:",
+                "Tool result:",
+                "The same tool, answered by a local keyword classifier:",
+                "local-keyword-classifier",
+            ],
+            ExpectedOutputDescription =
+            [
+                "Each of the three tickets should show a tool result with department, urgent, and frustration answers from a jev model, followed by a one-line agent reply.",
+                "The failing Stripe integration ticket should be routed to technical and marked urgent, and the invoice request should be routed to billing and not urgent.",
+                "The vague account ticket may be sent to human review when the department confidence is low.",
+                "The last part should show a tool result from the local-keyword-classifier model for the invoice ticket, followed by an agent reply.",
+                "The output should not contain error messages or stack traces.",
+            ],
+        },
+
         // ── AgentSkills ─────────────────────────────────────────────────────
 
         new SampleDefinition

@@ -93,6 +93,13 @@ covering basics, function tools, structured output, middleware, MCP, code interp
 | [Conversation](./openai/Agent_OpenAI_Step05_Conversation/) | Multi-turn conversations with OpenAI |
 | [Code Interpreter](./openai/Agent_OpenAI_Step06_CodeInterpreterFileDownload/) | Code interpreter with file downloads |
 
+### [TypeSafe](./typesafe/)
+
+| Sample | Description |
+| --- | --- |
+| [Agent with TypeSafe](./typesafe/Agent_With_TypeSafe/) | Use Jev, the TypeSafe System One model, as an IChatClient that answers typed questions and calls closed-set tools |
+| [Loop judge](./typesafe/Agent_TypeSafe_Step01_LoopJudge/) | Use Jev as the judge of a LoopAgent, with one yes or no question per criterion |
+
 ## Running the samples
 
 Navigate to a sample directory and run:

@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.TypeSafe\Microsoft.Agents.AI.TypeSafe.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,107 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample uses Jev, the TypeSafe System One model, as the judge of a LoopAgent. A Foundry agent writes the
+// answer. After each answer, Jev checks every criterion with its own yes or no question, and the loop sends the agent
+// the criteria that are still missing, until the answer meets all of them.
+
+#pragma warning disable MAAI001 // LoopAgent and DelegateLoopEvaluator are experimental.
+
+using System.ClientModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.TypeSafe;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var typeSafeApiKey = Environment.GetEnvironmentVariable("TYPESAFE_API_KEY") ?? throw new InvalidOperationException("TYPESAFE_API_KEY is not set.");
+
+// 1. The criteria. Each one becomes a Noul question, whose answer is the probability that the answer meets it, so the
+// judge reports exactly which criteria are missing instead of a single verdict. The questions describe what a good
+// answer contains; wording that steers the judge, such as "only say yes if", tends to push every answer one way.
+Dictionary<string, string> criteria = new()
+{
+    ["blue_sky"] = "explains why the daytime sky appears blue",
+    ["red_sunsets"] = "explains why sunsets appear red or orange",
+    ["plain_language"] = "uses clear language suitable for a general audience",
+};
+Dictionary<string, JevQuestion> questions = criteria.ToDictionary(
+    criterion => criterion.Key,
+    criterion => (JevQuestion)new JevNoulQuestion { Instructions = $"Does the assistant's latest response {criterion.Value}?" });
+
+// A criterion is met when its probability is above this threshold. 0.5 is not calibrated for any task: a higher value
+// accepts fewer answers that still miss a criterion, at the cost of more loop runs. Choose it on labeled examples of
+// your own answers.
+const double CriterionThreshold = 0.5;
+
+// 2. The agent that writes the answers. Its instructions hold back part of the first answer, so the judge has a gap
+// to find and the loop runs more than once.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent answerer = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential()).AsAIAgent(
+    model: deploymentName,
+    name: "Answerer",
+    instructions: """
+        You answer science questions for a general audience in one short paragraph.
+        In your first answer, explain only why the daytime sky is blue, even if the user asks for more.
+        When feedback lists missing points, rewrite the whole answer so that it covers them too.
+        """);
+
+// 3. The judge. AIJudgeLoopEvaluator asks its chat client for JSON structured output, which Jev cannot write, so a
+// DelegateLoopEvaluator asks Jev the criteria questions instead. Jev reads the original request and the latest answer
+// as a conversation.
+using var judge = new JevChatClient(new ApiKeyCredential(typeSafeApiKey));
+bool accepted = false;
+var evaluator = new DelegateLoopEvaluator(async (context, cancellationToken) =>
+{
+    Console.WriteLine($"\nAnswer {context.Iteration}:\n{context.LastResponse.Text}");
+    List<string> missing = await JudgeAsync(context.InitialMessages, context.LastResponse.Text, cancellationToken);
+    accepted = missing.Count == 0;
+
+    // The feedback names the missing criteria, which is the gap analysis that a generative judge would write.
+    return accepted
+        ? LoopEvaluation.Stop()
+        : LoopEvaluation.Continue($"Your answer does not yet: {string.Join("; ", missing)}. Rewrite the whole answer so that it does.");
+});
+
+// 4. The loop re-runs the agent until the judge stops it. MaxIterations bounds the cost if the answer never passes.
+const int MaxIterations = 3;
+const string Request = "Explain why the sky is blue and why sunsets are red.";
+AIAgent loop = new LoopAgent(answerer, evaluator, new LoopAgentOptions { MaxIterations = MaxIterations, NonStreamingReturnsLastResponseOnly = true });
+AgentResponse response = await loop.RunAsync(Request);
+
+// LoopAgent stops at MaxIterations before it calls the evaluator, so an answer from the last allowed run has not been
+// judged yet. Judging it here keeps the final answer from being presented as accepted when it may miss a criterion.
+if (!accepted)
+{
+    Console.WriteLine($"\nAnswer {MaxIterations} (the last run the loop allows):\n{response.Text}");
+    accepted = (await JudgeAsync([new ChatMessage(ChatRole.User, Request)], response.Text, CancellationToken.None)).Count == 0;
+}
+
+Console.WriteLine(accepted ? "\nFinal answer:" : "\nFinal answer (some criteria are still missing):");
+Console.WriteLine(response.Text);
+
+// Asks Jev whether the answer meets each criterion, prints the probabilities, and returns the missing criteria.
+async Task<List<string>> JudgeAsync(IEnumerable<ChatMessage> request, string answer, CancellationToken cancellationToken)
+{
+    List<ChatMessage> conversation = [.. request, new ChatMessage(ChatRole.Assistant, answer)];
+    ChatResponse verdict = await judge.GetResponseAsync(conversation, new ChatOptions().WithJevQuestions(questions), cancellationToken);
+    JevResult result = verdict.GetJevResult()!;
+
+    Console.WriteLine("Judge:");
+    List<string> missing = [];
+    foreach ((string id, string description) in criteria)
+    {
+        double probability = ((JevNoulResponse)result.Answers[id]).Noul;
+        bool met = probability > CriterionThreshold;
+        Console.WriteLine($"  {id}: {(met ? "met" : "missing")} (probability {probability:F2})");
+        if (!met)
+        {
+            missing.Add(description);
+        }
+    }
+
+    return missing;
+}