The Five Components Every Applied AI System Needs
Input, inference, decision logic, outcome, feedback. Here is what each one actually means in a system that runs in the real world.
Every applied AI system I build follows the same architecture. Not because I copied it from somewhere, but because after enough time building systems that run in real operational environments, the same five components keep showing up as necessary. Leave any one of them out and the system either does not work or does not last.
Here is what each one actually means in practice.
1. Input
Every system starts with a signal. A customer calling a service line. An order placed online. A lead coming through a contact form. Someone clicking an ad. Someone replying to an email.
The input is the data that enters the system, and the quality of everything downstream depends on how well you understand what that input looks like and how consistent it is. If your input is messy, inconsistent, or highly unique with no added context, the system will struggle before it even gets to the AI part.
This is why scoping a system starts with input clarity. What is coming in, from where, in what format, and how reliably? The answers to those questions shape every decision that follows.
2. Inference
Inference is what the model does with the input once it arrives. This is the AI component, the part where the system classifies, extracts, summarizes, scores, or recommends based on what it has received.
The critical thing to understand about inference is that it has limits. If the input is abstract, highly specialized, or outside what the model has been exposed to, it will hallucinate. It will produce a confident sounding answer that is wrong, not because the model is broken, but because it is working with information it does not have enough context to interpret correctly.
This is why understanding model capability is part of the build, not an afterthought. You need to know what the model can do reliably before you design the decision logic around it. Inference that works well in a demo can fail consistently in production if the real-world inputs are different from what was tested.
3. Decision Logic
Decision logic is where the business takes back control.
The model has interpreted the input. Now what? Decision logic is the set of rules that determine what happens next: route this to the correct department, flag this for human review, call another tool to do additional research, send this response, disqualify this lead, approve this request.
Think of it as ETL, extract, transform, load, applied to business rules. The input has been cleaned up and interpreted by the model. Decision logic applies the organization's actual requirements to that output and determines the path forward.
This is also where most of the operational thinking lives. What counts as qualified? What gets escalated? What can the system handle autonomously and what needs a person? Those decisions belong in the logic layer, not left to the model to figure out on its own.
4. Outcome
The outcome is what happens in the real world as a result of the system running.
A lead gets flagged and a salesperson follows up. A customer receives a personalized reorder email with a draft order ready to confirm. An unqualified inquiry gets a thoughtful response that does not burn the relationship. A support ticket gets routed to the right team without anyone touching it manually.
The outcome is what the whole system is built toward, and it is the only thing that matters when you are evaluating whether the system is working. Not whether the model is performing well in isolation. Not whether the logic is elegant. Whether the outcome is moving the organization toward its goal.
The outcome is also not always what you anticipated. Which is why the fifth component exists.
5. Feedback
Feedback is the component most organizations skip, and the one that determines whether a system improves or slowly degrades.
The question feedback answers is simple: is the outcome actually helping us reach the goal? Not just producing output, reaching the goal. Those are different things. A system can run perfectly, produce consistent outcomes, and still not move the metric that matters. Feedback is how you find out.
But there is a deeper reason feedback matters that most people do not talk about: applied AI systems are living systems. Every API you call, every model you use, every platform your system runs on is itself built on other APIs, other models, and other platforms. When one of those dependencies releases an update, it creates a ripple effect across the entire ecosystem.
Sometimes those changes are handled gracefully. A good vendor gives you advance notice, documents the deprecation timeline, and gives you time to prepare. Other times an update ships overnight and a system that was running cleanly is suddenly broken, with no warning and no plan.
This is why maintainability is not an afterthought. It is a design requirement. A system needs to be built so that when something changes, and something always changes, it can be diagnosed, updated, and restored without starting from scratch.
The same principle applies to the people who build and maintain the system. If the person who built it leaves, does the system keep running? Is it documented clearly enough for someone else to maintain? Is it built on stable, supported infrastructure or on something clever that only one person understands?
I have seen organizations end up stranded because an internal team member built something genuinely useful, then left, and the organization was left scrambling. They rebuild from scratch, reallocate resources that were already spoken for, and deal with critical infrastructure problems at the worst possible time.
A well-built system does not depend on any single person. That is not just good engineering practice. It is what separates a system from a tool.
The Framework in Practice
Input to inference to decision logic to outcome to feedback.
Each component has a job. Together they form a system that takes a real-world signal and turns it into a real-world result, reliably, repeatedly, and without requiring someone to manually manage it every time it runs.
That is the goal. Not AI that impresses in a demo. AI that holds up in production, improves over time, and keeps running when the people who built it are busy doing something else.
David Valencia is a full stack developer and systems thinker focused on applied AI systems and LLM discoverability. He works with organizations that want AI to produce outcomes, not just outputs. Minnesota.AI
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