Limits of LLM reasoning

Large language models are powerful reasoning components, but they are not magic. When we place an LLM inside an agent loop, we also inherit its constraints. Understanding those constraints is essential if we want to build systems that are reliable, predictable, and safe enough to run as real programs.

This lesson exists to make those limitations visible before we rely on an LLM for decisions that matter.

Non-deterministic behavior of LLMs

LLMs do not behave deterministically in the way traditional code does. Given the same prompt, the same state, and the same instructions, the model may still produce different outputs on different runs.

This matters because agent behavior can change without any code changes. An action chosen once may not be chosen again, even when conditions appear identical. In an agent loop, this means we must treat model output as a suggestion, not a guaranteed decision.

Sensitivity to prompt phrasing

Small changes in wording can produce meaningfully different results. Reordering sentences, changing emphasis, or rephrasing a constraint can alter how the model reasons about the same situation.

This sensitivity means prompts are part of the system’s logic. They must be treated with the same care as code, because they shape decisions just as directly. An unclear or ambiguous prompt often leads to unclear or inconsistent behavior.

Hallucinations and incorrect reasoning

LLMs can confidently produce incorrect information. They may invent facts, assume missing details, or reason from false premises without signaling uncertainty.

In an agent, this shows up as actions based on things that are not true. The model may claim a tool exists when it does not, assert state that was never provided, or justify a decision with flawed logic. This is not a bug to be fixed once; it is a property to be managed continuously.

Cost and latency considerations

Every call to an LLM has a cost and a response time. Even when responses are fast, they are slower than local code and not free to run at scale.

This affects how often we ask the model to reason. Calling an LLM on every minor decision can make a system expensive or sluggish. Designing agents requires deciding which decisions are worth paying for, and which should remain deterministic.

The need for guardrails and validation

Because LLM output can vary, be incorrect, or be poorly grounded, it cannot be trusted blindly. Guardrails are the structures that keep the program in control.

This includes validating model outputs, checking decisions against allowed actions, and enforcing constraints in code rather than in prose. The agent remains responsible for correctness; the model assists with reasoning, but it does not replace verification.

Conclusion

At this point, we are oriented to the practical limits of LLM reasoning. We know that outputs are non-deterministic, sensitive to phrasing, sometimes wrong, and not free to produce.

With these constraints in mind, we can use LLMs effectively—by placing them behind validation, surrounding them with deterministic logic, and treating them as fallible components inside a larger system rather than as the system itself.