Comparing agent types

Up to this point, we have built agents in two very different ways. One relies on explicit rules and deterministic logic. The other introduces an LLM as a reasoning component inside the agent loop. This lesson exists to help us understand the tradeoffs between these approaches so we can choose deliberately when building real AI-enabled systems.

Comparing decision-making approaches

Classical agents make decisions by evaluating rules written directly in code. Given the same state, they always reach the same conclusion. The decision process is transparent and fully controlled by the program.

LLM-based agents shift decision-making to a model. Instead of encoding rules, we provide context and ask the model to reason about what to do next. The program interprets the model’s output and acts on it. The logic is more flexible, but also less explicit.

Strengths and weaknesses of deterministic agents

Deterministic agents excel when rules are clear and stable. Their behavior is predictable, testable, and easy to reproduce. Debugging usually means inspecting state and following conditional logic.

Their weakness appears as complexity grows. Adding new cases often means adding more rules, which can become brittle and hard to maintain. They struggle when decisions depend on nuanced interpretation rather than clear conditions.

Strengths and weaknesses of LLM-driven agents

LLM-driven agents handle ambiguity well. They can interpret messy input, weigh competing factors, and generalize from context without explicit rules. This makes them well suited to open-ended tasks and natural language interaction.

Their weaknesses come from uncertainty. Decisions may vary across runs, and reasoning is harder to inspect. Models can produce incorrect or unexpected outputs, so additional validation and guardrails are required.

Cost, reliability, and complexity tradeoffs

Deterministic agents are cheap to run and highly reliable once implemented. Their cost is paid upfront in design and code complexity.

LLM-driven agents introduce ongoing costs for model calls and latency. They reduce code complexity in some areas but increase system complexity elsewhere, especially around validation, retries, and monitoring.

Choosing the right approach for a given problem

Neither approach is universally better. Deterministic agents are a strong choice when rules are well understood and correctness is critical. LLM-driven agents shine when flexibility, interpretation, or language understanding is central to the task.

In practice, many effective systems combine both. We keep sensing and acting deterministic, apply rules where safety matters, and use an LLM where reasoning benefits from adaptability.

Conclusion

At this point, we are oriented to the core differences between classical and LLM-based agents. We understand how their decision-making styles differ, where each approach excels, and what tradeoffs they introduce. With this perspective, we are better equipped to choose the right agent design for the problems we want to solve.