The artificial intelligence boom has given engineering and product teams an incredible new capability. When developers get their hands on a new tool, the natural instinct is to try it out on absolutely everything. You throw an LLM at a tough problem, and surprisingly, it actually works perfectly on the first try. Writing prompts feels like magic compared to mapping out rigid software logic and hunting down edge cases. But just because a tool can do a job does not mean it is the best tool for that job. Overusing generative AI for routine processes leads to systems that are more expensive, noticeably slower, and much less predictable.
To be clear, using AI to write the code for these repetitive tasks is a fantastic use of the technology that saves developers countless hours of typing. The mistake happens when we ask the AI to execute the task itself, at runtime, instead of just having it write the code.
To figure out why this is wrong, we have to define what AI actually does best. Modern models mainly excel in two areas: unstructured text and image processing, and runtime reasoning.
If you need a system to read a messy email from a frustrated user, extract the core complaint, and draft a polite response, AI is the best solution we have ever had. Traditional scripts would fail the moment they encountered a typo. The same applies to visual data. If you need to scan thousands of user-uploaded photos and flag the ones containing a specific object, modern multimodal AI handles this natively. Writing standard code to understand the context of a photograph is nearly impossible, making AI the clear choice.
Similarly, AI is incredible when a workflow requires dynamic decision making. These are scenarios where the next step is not explicitly defined beforehand. The system has to look at its environment, reason about the current state, and choose the best action. AI agents thrive here. They can handle changing conditions and unexpected details easily.
But what if the task is completely straightforward? This is where many teams fall into an architectural trap.
Think about a standard IT background process. You might need to scan a directory for log files, move them to a server, parse out error codes, and trigger an alert if the error rate spikes. This task does not require runtime reasoning. The steps are clear and can be programmed using deterministic code. Yet, we see teams trying to build AI agents to manage these exact workflows.
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Using AI for strictly deterministic tasks is, first and foremost, unnecessarily expensive. In the previous example, a standard Python script can move and parse ten thousand log files at negligible cost, whereas asking an AI model to read and interpret all those files can cost far more to do the exact same job. Beyond the massive cost difference, AI introduces unnecessary risk. The Python script will follow the exact same rules every single time. An agent might process most files correctly but hallucinate a creative new naming convention for one file just by chance. Finally, having the model read and interpret the logs will take significantly longer to process the batch. Running every step through an agentic prompt makes your system much more expensive, less reliable, and noticeably slower.
The solution is to architect systems that divide the work. Most business workflows are a hybrid of cognitive reasoning and mechanical execution.
The first option is to have code that calls AI. In this setup, traditional, hard-coded software acts as the main orchestrator. It runs on a schedule, performs deterministic logic, and only calls an LLM when it needs to do something like summarizing text, describing an image, or categorizing a document. The code does the predictable heavy lifting, and the AI provides a momentary cognitive assist.
The second option is to have an AI agent that calls code. In dynamic environments, an AI agent directs the flow. It reasons about the right thing to do at runtime. But once it makes a decision, it does not execute the action using AI. Instead, it triggers a strictly codified tool. If the agent decides a refund is necessary based on a customer conversation, it then calls pre-written, secure code to actually move the money and update the database.
At Adaptive6, we make this distinction a core part of our engineering philosophy. We know that businesses need absolute reliability, so we do not use AI in an unsupervised manner. We use AI only for non-production tasks, internal one-offs, or for product logic that cannot feasibly be codified, such as creating a PR that fixes a detected issue. Everything else, including data pipelines, standard API integrations, and repetitive operational loops, is built with robust, deterministic code.
When you need to create logic that needs to understand text or parse images, or if every run needs to reason over the input and take a significantly different execution path which is hard to predict and codify, LLMs and agentic flows are probably the right answer. But they do come with a price, both in terms of cost as well as execution speed and being non-deterministic. In many cases, it is possible to codify the logic or part of it, and the result is cheaper, faster, and more deterministic. Let the AI handle the complex reasoning, and let deterministic code handle the clear, rule-based logic.
Omer Müller is co-founder and CTO of Adaptive6.