.NET applications increasingly embed artificial intelligence to enhance capability, autAomate routine decisions, and support richer user experiences. Yet a recurring architectural mistake is to treat AI as a drop-in enhancement without considering where it should reside within the system.

Placing AI logic indiscriminately into the core execution path can create unpredictable behaviour, latency issues, and maintenance complexity. A more deliberate approach distinguishes between layers where AI adds value and layers where deterministic behaviour must be preserved.

Let's explore where AI belongs in .NET applications, why certain integration patterns tend to fail in production, and how architectural boundaries can balance capability with control without limiting future evolution.

The costs of indiscriminate AI placement

Many teams begin with the instinct to integrate AI deeply and early. It is tempting to invoke models directly from controllers, embed AI calls inside domain services, or distribute AI logic across multiple layers.

Early prototypes may appear to work, especially under controlled conditions with limited traffic and narrow input distributions.

As soon as usage scales or the model interacts with unpredictable real-world data, the limitations of ad hoc integration become clear:

• requests slow down unpredictably because each controller action depends on an external model call,
• errors and timeouts in AI services propagate directly into core application logic,
• behavioural drift manifests as functional regressions that tests do not catch,
• governance and traceability become difficult because model behaviour is not versioned or audited.

These symptoms are architectural rather than technical and result from mixing probabilistic behaviour with deterministic application logic without clear boundaries.

Core execution path versus advisory layers

To reason about where AI should live, it helps to separate two conceptual layers in an application:

Core execution path
This is the portion of the system that must execute deterministically to fulfil business contracts and service level guarantees. It includes request handling, transaction processing, compliance enforcement, and any logic where delay or inconsistency directly affects application correctness.

Examples where core path mistakes often occur:

• calling an LLM synchronously in a controller to make a real-time business decision,
• embedding model inference inside domain services without fallback or timeouts,
• relying on AI responses to format canonical business artefacts before persisting them.

Advisory and sidecar layers
These are layers where AI contributes insight, augmentation, enrichment, or suggestions without blocking or altering the main flow of execution. Advisory patterns respect the determinism of the core path while still benefiting from AI.

Scenarios that fit advisory or sidecar models:

• asynchronous enrichment of data (e.g., sentiment analysis stored separately),
• AI summaries displayed in user interfaces without affecting transactional logic,
• dashboards and analytics powered by model output but not gating primary workflows.

How to integrate AI in .NET without destabilising architecture

robust architecture embraces a separation of concerns between deterministic and probabilistic elements. Several patterns support this.

1. Use sidecar services for model interaction

Instead of embedding AI calls directly in controllers or services, introduce sidecar services that handle model interaction independently.

The sidecar can expose a well-defined API that the main application can call asynchronously or on demand, while the core path remains unaffected by AI latency or errors.

This approach has multiple benefits:

• the main application retains predictable performance,
• AI interaction can be versioned and monitored independently,
• fallback and retry logic can be encapsulated within the sidecar.

2. Introduce asynchronous processing for advisory tasks

Many AI use cases do not require synchronous responses. For example, tagging, summarisation, or classification can be processed asynchronously via background workers or message queues (e.g., Azure Service Bus, RabbitMQ). The core request completes quickly, then a worker enriches data and updates other services as needed.

3. Define explicit fallbacks and decoupled contracts

Where AI results affect user-facing features, define clear API contracts and fallback behaviour. For instance, if an AI service fails or times out, the API should return a deterministic default or a cached result rather than letting exceptions propagate into domain logic.

Contracts should include:

• explicit response shapes,
• confidence or metadata fields to indicate quality,
• fallbacks for missing or partial data.

4. Version and monitor AI artefacts

AI models, prompts, and configuration parameters are part of the behaviour surface. Treat them as first-class artefacts in your pipeline:

• assign explicit versions to models and prompts,
• include them in CI/CD pipelines for tracking,
• surface version metadata in logs for traceability.

Versioning makes it possible to roll back behaviour independently of code changes, which is critical when models update more frequently than application releases.

Illustrative example: real-time recommendations versus offline enrichment

Consider a .NET e-commerce platform that uses an AI model to generate personalised product recommendations.

A core path mistake would be calling the model synchronously inside the checkout controller to immediately tailor offers before completing the sale. This couples model latency to user experience and exposes the checkout flow to unpredictable behaviour.

A better pattern is to compute recommendations asynchronously when the user enters the site or during off-peak times, store them in a cache or database, and serve cached results from the core path. When model responses are slow or change, the core logic continues to function predictably, and the user still sees personalised content.

Blocshop principles and how we apply them

At Blocshop, we design AI-enhanced .NET applications with clear boundaries between deterministic and probabilistic behaviour. When working with teams, we focus on patterns that preserve responsiveness, predictability, and traceability: isolating AI interaction, enforcing decoupled contracts, and managing AI artefacts as part of delivery pipelines.

If you are exploring where AI should sit in your own .NET architecture, you can schedule a free consultation with Blocshop to discuss how these patterns apply to your systems and delivery pipelines and where we could help you optimize.