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AI agents can be the wrong tool for predictable work. If a task has structured inputs, exact rules, or a small set of explicit outcomes, ordinary code may be easier to verify and maintain than an LLM workflow. The claim that “half” of production agents are essentially if-statements is not backed by a survey or measurement in the article behind that headline; it is an opinion, not a prevalence statistic.
What the “half” claim does—and does not—show
In a September 28, 2026 DEV Community opinion article, Dimitris Kyrkos argues that teams sometimes route straightforward tasks through LLMs, vector databases, or agent loops when deterministic code would be simpler. The article offers examples, not production case studies or a survey, so it does not establish how common that design is or quantify its cost or performance effects. Read Kyrkos’s article.
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The useful question is narrower: is an LLM the simplest thing that does this reliably? A model’s ability to perform a task does not, by itself, make it the best component for that task.
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Extracting a fixed-format identifier
If an invoice identifier must match INV- followed by eight digits, a regular expression can check that format directly. Kyrkos suggests reserving an LLM for cases the rule cannot resolve. His article’s 98% example is hypothetical; it is not a measured extraction rate or benchmark.
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This approach is appropriate only when the format is genuinely stable and the system handles non-matches deliberately. A pattern can validate the shape of an identifier; it cannot establish that the identifier is genuine, belongs to the invoice, or is otherwise correct.
Finding exact records
For a request such as finding all unpaid orders for one customer within a date range, SQL filters can express the exact conditions and return records that can be checked and audited. Semantic or vector search is better suited to finding material by similarity or meaning, where the request does not map neatly to exact fields and values.
That distinction is a design recommendation in Kyrkos’s example, not the result of a comparative experiment. The right choice depends on whether the user needs complete, rule-bound results or approximate matches to a concept.
Routing among a small set of known outcomes
If support requests follow a small set of explicit conditions, ordinary branches can represent the routing logic directly. An agent that plans through tools and memory adds moving parts without necessarily adding useful judgment when all possible decisions are already enumerable.
Code is not automatically safer: poorly specified rules can produce poor outcomes, and a growing set of exceptions can become hard to manage. The point is to make decision logic explicit when it is already known, rather than using an agent loop merely because the workflow involves several steps.
How to decide whether a task needs a model
Before adding an LLM, agent loop, or semantic retrieval layer, work through these questions:
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- Are the inputs structured or the outputs fixed-format? If fields and valid formats are known, ordinary validation and parsing may be sufficient.
- Does the request ask for exact facts or for meaning? Exact filters and complete record sets favor structured queries; similarity and ambiguous language may call for semantic methods.
- Can you enumerate the logic? A short, stable decision tree is often easier to explain and reproduce as ordinary code.
- What happens when the system is wrong? Identify the impact, how an error will be detected, and whether a person can review or correct the result.
- Who will maintain the design later? Account for dependencies, failure handling, reproducibility, and the effort required to change the workflow—not just whether a model can complete a demonstration.
For an existing workflow, compare the current and proposed designs on task accuracy, latency, cost, failure handling, reproducibility, and maintenance burden. Kyrkos’s article provides no measurements for those factors, so teams need to evaluate them in their own context rather than assume a model or a rule-based system will win.
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Where AI still makes sense
Kyrkos’s argument is not “never use AI.” He draws the boundary around ambiguity: unstructured text, fuzzy matching, and generation are examples of work where a model may contribute capabilities that fixed rules do not provide. Deterministic rules remain useful around that work—for validating outputs, enforcing required fields, or routing cases that need review.
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The aim is not to remove models from a system at all costs. It is to use them where interpretation or generation is needed, and avoid making them the default for tasks whose rules are already precise.
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