Adding AI Capabilities to an Existing .NET Application Incrementally

1. Choose an assistive first capability

Begin with a task where a user can review the result before it changes business state: summarize a case, draft a reply, classify a document, extract candidate fields, or suggest the next step. These capabilities create measurable value while preserving accountability.

Avoid starting with unrestricted agents or broad autonomous actions. Define the input, permitted sources, output contract, expected latency, cost ceiling, and safe failure behavior. Capture the current human time and quality baseline before implementation.

2. Introduce a provider-neutral application boundary

Register AI clients through dependency injection and keep model-specific code in infrastructure adapters. Microsoft.Extensions.AI provides abstractions such as IChatClient and IEmbeddingGenerator, plus middleware patterns for telemetry, caching, and function invocation.

Your domain or application service should request a business capability such as ICaseSummaryService, not construct prompts or depend directly on one provider SDK. That boundary makes testing easier and lets the team change models, deployment options, or orchestration without rewriting controllers and workflows.

• Define typed request and result contracts
• Keep prompt templates versioned outside controllers
• Wrap provider exceptions in application-level outcomes
• Pass cancellation and enforce explicit timeouts
• Use structured output when downstream code consumes the result

3. Keep deterministic rules and authorization in control

Use AI for interpretation or generation, then validate its output with normal .NET code. Required fields, monetary limits, permissions, workflow transitions, and compliance rules should remain deterministic. A model response is untrusted input until the application validates it.

Resolve the current user and authorized data before constructing context. Tools must call existing application services that already enforce business invariants rather than write directly to tables. This lets the AI capability reuse the security boundary the application has earned over time.

4. Build evaluation and observability into the client pipeline

Create a small approved dataset that represents normal, edge, missing-information, and unsafe cases. Score task accuracy, completeness, unsupported claims, structured-output validity, safety, latency, token usage, and human correction effort.

Trace the AI operation beside the existing request trace. Record model and prompt versions, duration, token counts, retries, tool calls, validation failures, and user feedback without exposing sensitive prompt content in unrestricted logs. Evaluate changes before replacing a model or prompt in production.

• Mock the application-facing interface in unit tests
• Run model-backed evaluations separately from deterministic tests
• Use correlation identifiers across API, model, and tools
• Define quality and cost release thresholds
• Monitor fallback and human-correction rates

5. Release behind flags and expand by evidence

Use a feature flag to expose the capability to internal users or a small cohort. Preserve the original workflow and make fallback visible. Collect structured feedback that distinguishes poor source data, retrieval, instructions, model behavior, user experience, and integration failures.

Expand autonomy in stages: suggestion, editable draft, confirmed action, and only then bounded automatic action where risk permits. Each stage needs stronger authorization, idempotency, auditing, retry, and compensation behavior. The application should remain useful when the AI service is slow or unavailable.