001 Technology Selection

---
name: technology-selection
description: "Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference)."
license: MIT
---

# .NET AI and Machine Learning

## Inputs

| Input | Required | Description |
|-------|----------|-------------|
| Task description | Yes | What the AI/ML feature should accomplish (e.g., "classify support tickets", "summarize documents") |
| Data description | Yes | Type and shape of input data (structured/tabular, unstructured text, images, mixed) |
| Deployment constraints | No | Cloud vs. local, latency SLO, cost budget, offline requirements |
| Existing project context | No | Current .csproj, existing packages, target framework |

## Workflow

### Step 1: Classify the task using the decision tree

Evaluate the developer's task against this decision tree and select the appropriate technology. State which branch applies and why.

| Task type | Technology | Rationale |
|-----------|-----------|-----------|
| Structured/tabular data: classification, regression, clustering, anomaly detection, recommendation | **ML.NET** (`Microsoft.ML`) | Reproducible (given a fixed seed and dataset), no cloud dependency, purpose-built models for these tasks |
| Natural language understanding, generation, summarization, reasoning over unstructured text (single prompt → response, no tool calling) | **LLM via Microsoft.Extensions.AI** (`IChatClient`) | Requires language model capabilities beyond pattern matching; no orchestration needed |
| Agentic workflows: tool/function calling, multi-step reasoning, agent loops, multi-agent collaboration | **Microsoft Agent Framework** (`Microsoft.Agents.AI`) built on top of **Microsoft.Extensions.AI** | Requires orchestration, tool dispatch, iteration control, and guardrails that `IChatClient` alone does not provide |
| Building GitHub Copilot extensions, custom agents, or developer workflow tools | **GitHub Copilot SDK** (`GitHub.Copilot.SDK`) | Integrates with the Copilot agent runtime for IDE and CLI extensibility |
| Running a pre-trained or fine-tuned custom model in production | **ONNX Runtime** (`Microsoft.ML.OnnxRuntime`) | Hardware-accelerated inference, model-format agnostic |
| Local/offline LLM inference with no cloud dependency | **OllamaSharp** with local [AI models supported by Ollama](https://ollama.com/search) | Privacy-sensitive, air-gapped, or cost-constrained scenarios |
| Semantic search, RAG, or embedding storage | **Microsoft.Extensions.VectorData.Abstractions** + a vector database provider (e.g., Azure AI Search, Milvus, MongoDB, pgvector, Pinecone, Qdrant, Redis, SQL) | Provider-agnostic abstractions for vector similarity search; pair with a database-specific connector package (many are moving to community toolkits) |
| Ingesting, chunking, and loading documents into a vector store | **Microsoft.Extensions.AI.DataIngestion** (preview) + **Microsoft.Extensions.VectorData.Abstractions** (MEVD) | Handles document parsing, text chunking, embedding generation, and upserting into a vector database; pairs with Microsoft.Extensions.VectorData.Abstractions |
| Both structured ML predictions AND natural language reasoning | **Hybrid**: ML.NET for predictions + LLM for reasoning layer | Keep loosely coupled; ML.NET handles reproducible scoring, LLM adds explanation |

**Critical rule:** Do NOT use an LLM for tasks that ML.NET handles well (classification on tabular data, regression, clustering). LLMs are slower, more expensive, and non-deterministic for these tasks.

### Step 1b: Select the correct library layer

After identifying the task type, select the right library layer. These libraries form a stack — each builds on the one below it. Using the wrong layer is a major source of non-deterministic agent behavior.

| Layer | Library | NuGet package | Use when |
|-------|---------|---------------|----------|
| **Abstraction** | Microsoft.Extensions.AI (MEAI) | `Microsoft.Extensions.AI` | You need a provider-agnostic interface for chat, embeddings, or tool calling. This is the foundation — always include it. Use `IChatClient` directly **only** for simple prompt-in/response-out scenarios with no tool calling or agentic loops. If the task involves tools, agents, or multi-step reasoning, you must add the Orchestration layer above. |
| **Provider SDK** | OpenAI, Azure.AI.OpenAI, Azure.AI.Inference, OllamaSharp | `OpenAI`, `Azure.AI.OpenAI`, `Azure.AI.Inference`, `OllamaSharp` | You need a concrete LLM provider implementation. These wire into MEAI via `AddChatClient`. Use `OpenAI` for direct OpenAI access, `Azure.AI.OpenAI` for Azure OpenAI, `Azure.AI.Inference` for Azure AI Foundry / GitHub Models, or `OllamaSharp` for local Ollama. Use directly only if you need provider-specific features not exposed through MEAI. |
| **Orchestration** | Microsoft Agent Framework | `Microsoft.Agents.AI` (prerelease) | The task involves tool/function calling, agentic loops, multi-step reasoning, multi-agent coordination, durable context, or graph-based workflows. **This is required whenever the scenario involves agents or tools — do not hand-roll tool dispatch loops with `IChatClient`.** Builds on top of MEAI. **Note:** This package is currently prerelease — use `dotnet add package Microsoft.Agents.AI --prerelease` to install it. |
| **Copilot integration** | GitHub Copilot SDK | `GitHub.Copilot.SDK` | You are building extensions or tools that integrate with the GitHub Copilot runtime — custom agents, IDE extensions, or developer workflow automation that leverages the Copilot agent platform. |

#### Decision rules for library selection

1. **Start with MEAI.** Every AI integration begins with `Microsoft.Extensions.AI` for the `IChatClient` / `IEmbeddingGenerator` abstractions. This ensures provider-swappability and testability.

2. **Add a provider SDK** (`OpenAI`, `Azure.AI.OpenAI`) as the concrete implementation behind MEAI. Do not call the provider SDK directly in business logic — always go through the MEAI abstraction.

3. **Use Agent Framework (`Microsoft.Agents.AI`) for any task that involves tools or agents.** If the task is a single prompt → response with no tool calling, MEAI is sufficient. **You MUST use `Microsoft.Agents.AI`** when any of these apply:
   - Tool/function calling (agent decides which tools to invoke)
   - Multi-step reasoning with state carried across turns
   - Agentic loops that iterate until a goal is met
   - Multi-agent collaboration with handoff protocols
   - Graph-based or durable workflows

   Do **not** implement these patterns by hand with `IChatClient` — the Agent Framework provides iteration limits, observability, and tool dispatch that are error-prone to reimplement.

4. **Add Copilot SDK only when building Copilot extensions.** Use `GitHub.Copilot.SDK` when the goal is to build a custom agent or tool that runs inside the GitHub Copilot platform (CLI, IDE, or Copilot Chat). This is not a general-purpose LLM orchestration library — it is specifically for Copilot extensibility.

5. **Never skip layers.** Do not use Agent Framework without MEAI underneath. Do not call `HttpClient` to OpenAI alongside MEAI in the same workflow. Each layer depends on the one below it.

### Step 2: Select packages and set up the project

Install only the packages needed for the selected technology branch. Do not mix competing abstractions.

#### Classic ML packages

```xml
<PackageReference Include="Microsoft.ML" Version="4.*" />
<PackageReference Include="Microsoft.ML.AutoML" Version="0.*" />
<!-- Only if custom numerical work is needed: -->
PackageReference Include="System.Numerics.Tensors" Version="10.*"
<PackageReference Include="MathNet.Numerics" Version="5.*" />
<!-- Only for data exploration: -->
<PackageReference Include="Microsoft.Data.Analysis" Version="0.*" />
```

> **Do NOT use** Accord.NET — it is archived and unmaintained.

#### Modern AI packages

```xml
<!-- Always start with the abstraction layer -->
<PackageReference Include="Microsoft.Extensions.AI" Version="9.*" />

<!-- Orchestration (agents, workflows, tools, memory) — prerelease; use dotnet add package Microsoft.Agents.AI --prerelease -->
<PackageReference Include="Microsoft.Agents.AI" Version="1.*-*" />

<!-- Cloud LLM provider (pick one) -->
<PackageReference Include="Azure.AI.OpenAI" Version="2.*" />
<!-- OR -->
<PackageReference Include="OpenAI" Version="2.*" />

<!-- Client-side token counting for cost management -->
    <PackageReference Include="Microsoft.ML.Tokenizers" Version="2.*" 

<!-- Local LLM inference -->
<PackageReference Include="OllamaSharp" Version="5.*" />

<!-- Custom model inference -->
<PackageReference Include="Microsoft.ML.OnnxRuntime" Version="1.*" />

<!-- Vector store abstraction -->
<PackageReference Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.*" />

<!-- Document ingestion, chunking, and vector store loading (preview) -->
<PackageReference Include="Microsoft.Extensions.AI.DataIngestion" Version="9.*-*" />

<!-- Copilot platform extensibility -->
<PackageReference Include="GitHub.Copilot.SDK" Version="1.*" />
```

> **Stack coherence rule:** Never mix raw SDK calls (`HttpClient` to OpenAI) with `Microsoft.Extensions.AI`, Microsoft Agent Framework, or Copilot SDK in the same workflow. Pick one abstraction layer per workflow boundary and commit to it. See Step 1b for the layering rules.

#### Register services with dependency injection

All AI/ML services must be registered via DI. Never instantiate clients directly in business logic.

```csharp
// Configuration via IOptions<T>
services.Configure<AiOptions>(configuration.GetSection("AI"));

// Register the AI client through the abstraction
services.AddChatClient(builder => builder
    .UseOpenAIChatClient("gpt-4o-mini-2024-07-18"));
```

### Step 3: Implement with guardrails

Apply the guardrails for the selected technology branch. Every generated implementation must follow these rules.

#### Classic ML guardrails

1. **Reproducibility**: Always set a random seed in the ML context:
   ```csharp
   var mlContext = new MLContext(seed: 42);
   ```

2. **Data splitting**: Always split into train/test (and optionally validation). Never evaluate on training data:
   ```csharp
   var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.2);
   ```

3. **Metrics logging**: Always compute and log evaluation metrics appropriate to the task:
   ```csharp
   var metrics = mlContext.BinaryClassification.Evaluate(predictions);
   logger.LogInformation("AUC: {Auc:F4}, F1: {F1:F4}", metrics.AreaUnderRocCurve, metrics.F1Score);
   ```

4. **AutoML first**: Prefer `mlContext.Auto()` for initial model selection, then refine manually.

5. **PredictionEngine pooling**: In ASP.NET Core, always use the pooled prediction engine — never a singleton:
   ```csharp
   services.AddPredictionEnginePool<ModelInput, ModelOutput>()
       .FromFile(modelPath);
   ```

#### LLM integration guardrails

1. **Temperature**: Always set explicitly. Use `0` for factual/deterministic tasks:
   ```csharp
   var options = new ChatOptions
   {
       Temperature = 0f,
       MaxOutputTokens = 1024,
   };
   ```

2. **Structured output**: Always parse LLM output into strongly-typed objects with fallback handling:
   ```csharp
   var result = await chatClient.GetResponseAsync<MySchema>(prompt, options, cancellationToken);
   ```

3. **Retry logic**: Always implement retry with exponential backoff:
   ```csharp
   services.AddChatClient(builder => builder
       .UseOpenAIChatClient(modelId)
       .Use(new RetryingChatClient(maxRetries: 3)));
   ```

4. **Cost control**: Always estimate and log token usage. Use Microsoft.ML.Tokenizers to count tokens client-side before sending requests so you can enforce budgets proactively. Choose the smallest model tier that meets quality requirements (e.g., gpt-4o-mini before gpt-4o).

5. **Secret management**: Never hardcode API keys. Use Azure Key Vault, user-secrets, or environment variables:
   ```csharp
   var apiKey = configuration["AI:ApiKey"]
       ?? throw new InvalidOperationException("AI:ApiKey not configured");
   ```

6. **Model version pinning**: Specify exact model versions to reduce behavioral drift:
   ```csharp
   // Pin to a specific dated version, not just "gpt-4o"
   var modelId = "gpt-4o-2024-08-06";
   ```

#### Agentic workflow guardrails

0. **Use `Microsoft.Agents.AI` for all agentic workflows.** Do not implement tool dispatch loops or multi-step agent reasoning by hand with `IChatClient`. The Agent Framework provides `ChatClientAgent` (or `AgentWorker`) which handles the tool call → result → re-prompt cycle with built-in guardrails. All rules below assume you are using `Microsoft.Agents.AI`.

1. **Iteration limits**: Always cap agentic loops to prevent runaway execution:
   ```csharp
   var settings = new AgentInvokeOptions
   {
       MaximumIterations = 10,
   };
   ```

2. **Cost ceiling**: Implement a token budget per execution and terminate when reached. Use Microsoft.ML.Tokenizers to count prompt and completion tokens locally and compare against the budget before each iteration.

3. **Observability**: Log non-sensitive metadata for every agent step. Never log raw `message.Content` — it may contain user prompts, tool outputs, secrets, or PII that persist in plaintext in central logging systems:
   ```csharp
   await foreach (var message in agent.InvokeStreamingAsync(history, settings))
   {
       logger.LogDebug("Agent step: Role={Role}, ContentLength={Length}",
           message.Role, message.Content?.Length ?? 0);
   }
   ```

4. **Tool schemas**: Define explicit tool/function schemas with descriptions. Never rely on implicit tool discovery.

5. **Simplicity preference**: Prefer single-agent with tools over multi-agent unless the task genuinely requires agent collaboration.

#### RAG guardrails

1. **Embedding caching**: Never re-embed the same content on every query. Cache embeddings in the vector store.

2. **Chunking strategy**: Use semantic chunking (split on paragraph/section boundaries) over fixed-size chunking. Ensure chunks have enough context to be useful on their own.

3. **Relevance thresholds**: Do not inject low-relevance chunks into context. Set a minimum similarity score:
   ```csharp
   var results = await vectorStore.SearchAsync(query, new VectorSearchOptions
   {
       Top = 5,
       MinimumScore = 0.75f,
   });
   ```

4. **Source attribution**: Track which chunks contributed to the final response. Include source references in the output.

5. **Batch embeddings**: Batch embedding API calls where possible to reduce latency and cost.

### Step 4: Handle non-determinism

When the solution involves LLM calls or agentic workflows, explicitly address non-determinism:

1. **Acknowledge it**: Inform the developer that LLM outputs are non-deterministic even at temperature 0 (due to batching, quantization, and model updates).

2. **Validate outputs**: Implement schema validation and content assertion checks on every LLM response.

3. **Graceful degradation**: Design a fallback path for when the LLM returns unexpected, malformed, or empty output:
   ```csharp
   var response = await chatClient.GetResponseAsync<ClassificationResult>(prompt, options);
   if (response is null || !response.IsValid())
   {
       logger.LogWarning("LLM returned invalid response, falling back to rule-based classifier");
       return ruleBasedClassifier.Classify(input);
   }
   ```

4. **Evaluation harness**: For any prompt that will be iterated on, recommend creating a golden dataset and evaluation scaffold to measure prompt quality over time.

5. **Model version pinning**: Pin to specific dated model versions (e.g., `gpt-4o-2024-08-06`) to reduce drift between deployments.

### Step 5: Apply performance and cost controls

1. **Connection pooling**: Use `IHttpClientFactory` and DI-managed clients for all external services.

2. **Response caching**: Cache repeated or similar queries. Consider semantic caching for LLM responses where appropriate.

3. **Streaming**: Use `IAsyncEnumerable` for LLM responses in user-facing scenarios to reduce time-to-first-token:
   ```csharp
   await foreach (var update in chatClient.GetStreamingResponseAsync(prompt, options))
   {
       yield return update.Text;
   }
   ```

4. **Health checks**: Implement health checks for external AI service dependencies:
   ```csharp
   services.AddHealthChecks()
       .AddCheck<OpenAIHealthCheck>("openai");
   ```

5. **ML.NET prediction pooling**: In web applications, always use `PredictionEnginePool<TIn, TOut>`, never a single `PredictionEngine` instance (it is not thread-safe).

### Step 6: Validate the implementation

1. Build the project and verify no warnings:
   ```bash
   dotnet build -c Release -warnaserror
   ```

2. Run tests, including integration tests that validate AI/ML behavior:
   ```bash
   dotnet test -c Release
   ```

3. For ML.NET pipelines, verify that evaluation metrics meet the project's quality bar and that the model can be serialized and loaded correctly.

4. For LLM integrations, verify that structured output parsing handles both valid and malformed responses.

5. For RAG pipelines, verify that retrieval returns relevant results and that irrelevant chunks are filtered out.

## Validation

- [ ] Technology selection follows the decision tree — LLMs are not used for tasks ML.NET handles
- [ ] All AI/ML services are registered via dependency injection
- [ ] Configuration uses `IOptions<T>` pattern — no hardcoded values
- [ ] API keys are loaded from secure sources — not in source code or committed config files
- [ ] ML.NET pipelines set a random seed and split data for evaluation
- [ ] LLM calls set temperature, max tokens, and retry logic explicitly
- [ ] Agentic workflows have iteration limits and cost ceilings
- [ ] RAG pipelines implement chunking, relevance thresholds, and source attribution
- [ ] Non-deterministic outputs have validation and fallback paths
- [ ] `dotnet build -c Release -warnaserror` completes cleanly

## Anti-Patterns to Reject

When reviewing or generating code, flag and redirect the developer if any of these patterns are detected:

| Anti-pattern | Redirect |
|-------------|----------|
| Using an LLM for classification on structured/tabular data | Use ML.NET instead — it is faster, cheaper, and deterministic |
| Calling LLM APIs without retry or timeout logic | Add `RetryingChatClient` or Polly-based retry with exponential backoff |
| Storing API keys in `appsettings.json` committed to source control | Use user-secrets (dev), environment variables, or Azure Key Vault (prod) |
| Using Accord.NET for new projects | Migrate to ML.NET — Accord.NET is archived and unmaintained |
| Building custom neural networks in .NET from scratch | Use a pre-trained model via ONNX Runtime or call an LLM API |
| RAG without chunking strategy or relevance filtering | Implement semantic chunking and set a minimum similarity score threshold |
| Agentic loops without iteration limits or cost ceilings | Add `MaximumIterations` and a token budget ceiling |
| Using MEAI `IChatClient` with raw `HttpClient` calls to the same provider | Pick one abstraction layer and commit to it |
| Implementing tool calling or agentic loops manually with `IChatClient` instead of using `Microsoft.Agents.AI` | Use `Microsoft.Agents.AI` — it provides iteration limits (`MaximumIterations`), built-in tool dispatch, observability hooks, and cost controls. Hand-rolled loops lack these guardrails. |
| Using Agent Framework for a single prompt→response call | Use MEAI `IChatClient` directly — Agent Framework is for multi-step orchestration |
| Using Copilot SDK for general-purpose LLM apps | Copilot SDK is for Copilot platform extensions only — use MEAI + Agent Framework for standalone apps |
| Calling OpenAI SDK directly in business logic instead of through MEAI | Register the provider via `AddChatClient` and depend on `IChatClient` in business code |
| Using `PredictionEngine` as a singleton in ASP.NET Core | Use `PredictionEnginePool<TIn, TOut>` — `PredictionEngine` is not thread-safe |
| Using `Func<ReadOnlySpan<T>>` for delegates with ref struct parameters | Define a custom delegate type — ref structs cannot be generic type arguments |
| Using `Microsoft.SemanticKernel` for new projects | Use `Microsoft.Extensions.AI` + `Microsoft.Agents.AI` — Semantic Kernel is superseded by these newer abstractions for LLM orchestration and tool calling |

## Common Pitfalls

| Pitfall | Solution |
|---------|----------|
| Over-engineering with LLMs | Start with the simplest approach (rules, ML.NET) and add LLM capability only when simpler methods fall short |
| Evaluating ML models on training data | Always use `TrainTestSplit` and report metrics on the held-out test set |
| LLM output drift between deployments | Pin to specific dated model versions (e.g., `gpt-4o-2024-08-06`) |
| Token cost surprises | Set `MaxOutputTokens`, use Microsoft.ML.Tokenizers for accurate client-side token counting, log token counts per request, and alert on budget thresholds |
| Non-reproducible ML training | Set `MLContext(seed: N)` and version your training data alongside the code |
| RAG returning irrelevant context | Set a minimum similarity score and limit the number of injected chunks |
| Cold start latency on ML.NET models | Pre-warm the `PredictionEnginePool` during application startup |
| Microsoft Agent Framework + raw OpenAI SDK in same class | Choose one orchestration layer per workflow boundary |