AI agents are not the right fit for every enterprise task, analysis says

The central issue is nondeterministic behavior. Today's agents generally depend on large language models, which take human language inputs and produce human language outputs by predicting the next word in a sequence. Given how the models are trained, repeating a given input may produce different outputs, so LLM-based agents are inherently nondeterministic.

That nondeterminism gives agents their unique power. Without it, agents would not have agency: the ability to make decisions based on available information and take different actions to accomplish goals. Software without agency is not an agent by definition, although the analysis notes there are examples of agent-washing in which deterministic software is called an agent. Other nondeterministic software includes reinforcement learning and Monte Carlo simulations, which make decisions based on randomization. Before the agentic wave, however, the vast majority of software was deterministic: for given inputs, it always did the same thing. Predictable behavior is why software was built for bank transactions, payroll, airline seating and millions of other tasks.

Deterministic software is best when there is a right answer and the goal is to find it. Payroll is an example: the goal is to ensure everyone gets the correct pay. LLM-based nondeterministic reasoning makes the most sense when the best result is wanted from a range of different or changing inputs, even though there may not be a single right answer. Examples include weather-dependent planning, incident mitigation in complex IT environments and business forecasts based on multiple inputs. In such situations, there is no single correct answer, and the answer an agent produces may or may not be the best result. There is always a chance an agent will give a poor answer or take an undesirable action. If an organization does not want to take that gamble, the analysis says, it should not use agents.

The analysis identifies several situations in which using AI agents is a bad idea. When deterministic software meets an organization's needs, agents are generally a poor choice because they can misbehave and can be expensive to run. When every decision or action must be exactly correct, agents should not be allowed to touch payroll. When agentic misbehavior can cause serious harm, even a low probability of an agent going off the rails may not be worth the risk if it could lead to a dead hospital patient, a crashing airplane or an exploding oil rig. When agents are too expensive relative to the business benefits sought, they do not make economic sense, because they may make several LLM calls, retry various actions and invoke expensive tools. When human qualities are essential, LLMs and therefore agents can simulate empathy, creativity, insight and understanding, but they are only mimicking human behavior, and anthropomorphizing agentic behavior will backfire. The analysis says the anthropomorphism of agentic behavior, and of AI generally, is a subtle and poorly understood risk.

Editorial Summary: AI agents are widely used in 2026 but rely on nondeterministic large language models, making them unsuitable for tasks that require exact correctness, low cost or human qualities. The SiliconANGLE analysis says deterministic software remains the better choice when a single right answer is required, and it warns that anthropomorphizing agents carries underappreciated risk.