Replacing deterministic decisions with LLM reasoning

Modern agent systems often reach a point where rigid, rule-based decision logic starts to strain. This lesson exists to show how an LLM can take over decision-making inside an agent loop, while the rest of the program remains firmly under our control. The goal is not to make the agent mysterious or autonomous, but to make it more flexible without sacrificing reliability.

Identifying which decisions can move to an LLM

In a classical agent loop, not every step benefits from model reasoning. Sensing input, updating state, and performing actions usually work best when they are explicit and predictable. Decision points, on the other hand, often involve weighing context, intent, or loosely structured information.

These decision points are the natural candidates for replacement. Instead of encoding dozens of conditional rules, we can ask an LLM to choose among known options based on the current state and input.

Keeping sensing and acting deterministic

Even when an LLM is involved, the agent still needs firm boundaries. Input collection, file access, network calls, and state updates should behave the same way every time. This keeps the system observable and debuggable.

The LLM never senses the world directly and never performs actions itself. It only receives a structured snapshot of state and returns a decision that the program may choose to act on.

Using an LLM only for decision-making

At its core, the LLM replaces a decide() function. Instead of branching logic written in Python, we ask the model to select the next action from a known set.

decision = decide_with_llm(state_summary, available_actions)

The rest of the loop remains unchanged. We still call concrete tools, update state explicitly, and control when the loop continues or stops. The model’s role is narrow and well-defined.

Combining rule-based guards with LLM decisions

LLM output should never be trusted blindly. Simple rule-based checks act as guardrails around the model’s suggestions. These checks confirm that the chosen action is valid, allowed, and safe in the current context.

If a model proposes something unexpected, the program can reject it, fall back to a default, or ask the model again with tighter constraints. Rules and reasoning work together, rather than competing.

Preserving control over agent behavior

Replacing deterministic decisions does not mean giving up control. The program still owns the loop, the state, and the tools. The LLM contributes judgment, not authority.

By isolating model reasoning to a single decision step, we keep the agent understandable. We can inspect inputs, log outputs, and refine prompts without rewriting the surrounding system.

Conclusion

At this point, we have seen how an LLM can step into a classical agent loop without taking it over. Decision-making becomes more flexible, while sensing, acting, and state management remain solid and predictable. With this structure in place, we are ready to assemble a complete LLM-driven agent that behaves coherently over time.