Continuous integration and continuous delivery for AI applications has become a major focus of technical strategy. But a more foundational architectural question often goes unasked until too late: should AI actually be in the core path of your application at all?

Generative models and decision systems can offer powerful capabilities, but their uncertainty and cost characteristics do not always align with the functional requirements of critical flows.

Treating AI as an indispensable part of the main execution path can turn what seems like enhancement into what feels like liability once the system scales or operates under real constraints.

Let's explore when AI should remain advisory or decoupled rather than part of the core execution flow, why that distinction matters, and how teams can design systems that get value from AI without sacrificing reliability, performance, or control.

Why the “AI in the main path” instinct is so strong

There are solid reasons teams rush to integrate AI into core workflows.

The promise of automation and reduced manual effort, the appeal of richer user experiences, and vendor messaging that positions AI as a drop-in replacement for logic all reinforce the idea that intelligence belongs everywhere. In early development, these assumptions can feel validated. Prototypes with AI in the main path often function well under limited load and constrained input distributions.

As soon as the system is exposed to real users, however, edge cases, performance variability, and behaviour drift begin to surface. These symptoms are not bugs but direct consequences of embedding a probabilistic system where deterministic execution used to suffice.

Core versus advisory: a useful distinction

To reason about integration patterns, it helps to separate two conceptual roles that AI can play in an application:

1. Core execution path
In this model, the application depends directly on AI outputs to continue normal operation. Examples include real-time decision making such as credit scoring, recommendations that alter control flow, or model outputs used to generate canonical business artefacts.

2. Advisory or sidecar usage
Here, AI enriches behaviour without blocking or altering critical logic. Examples include suggested alternatives that do not change outcomes, summaries presented to users without affecting process execution, or asynchronous enrichment updating secondary artefacts.

This distinction may appear subtle, but it has profound operational consequences. Systems that treat AI as advisory can still benefit from models while retaining predictable behaviour in core paths.

When embedding AI in the core path becomes a liability

1. Unpredictable latency and throughput

Models do not have consistent service times. Even with batching or caching, inference is affected by load, cold starts, and provider variability. In core paths, this variability directly impacts user-visible performance.

2. Probabilistic outputs and downstream risk

When decision logic depends on model responses, outcomes can change without code changes. In regulated or safety-critical systems, this behaviour drift creates exposure that traditional testing cannot fully mitigate.

3. Cost and resource consumption

AI embedded in the main path often scales linearly with user activity. Advisory patterns provide more control over invocation frequency and cost impact.

4. Testing and governance overhead

Core-path AI requires extensive behavioural testing and additional governance gates. Each deployment becomes a potential behavioural change event.

How to design around the AI liability

• Prefer advisory over authoritative integration
  Keeping AI outputs advisory allows systems to benefit from intelligence without surrendering control. Suggestions can inform users or downstream analysis without blocking execution.
  
• Isolate AI in sidecar or batch processes
  Architecturally, placing AI in sidecar services or batch workflows keeps it out of latency-sensitive paths. Behaviour changes can be observed and evaluated without destabilising the core system.
  
• Define explicit fallback strategies
  Where AI must be in the main path, deterministic fallback behaviour must be clearly defined. This includes handling timeouts, degraded confidence, or unexpected outputs.

Illustrative example: advisory AI versus core dependency

Consider a customer service application that uses an LLM to prioritise incoming tickets. In a core-path integration, every ticket waits for a live model score before routing. If the model slows or changes behaviour, the entire system is affected.

In an advisory setup, tickets are routed deterministically on arrival. The model runs asynchronously, updates priority metadata later, and informs staffing or reporting decisions without blocking routing. The system remains predictable, while AI still adds value.

This is not an argument against using AI, but for situational integration.

Deterministic systems still require environment separation, reproducible deployment, observability, and disciplined versioning. These fundamentals remain necessary whether AI is advisory or core.

Blocshop approach

At Blocshop, we design and deliver AI-enabled applications with clear boundaries between deterministic execution and probabilistic behaviour.

When working with teams, we apply these same principles to their existing systems, focusing on where AI adds value without introducing unnecessary operational risk.

If you are assessing where AI should sit in your own application flow, you can schedule a free consultation with Blocshop to discuss how these patterns apply to your architecture and delivery pipeline.