COMPARISON
Both designs put a language model and business rules in the same system. They differ on one question: which of the two is allowed to decide what happens. Everything else follows from that.
THE ARCHITECTURAL DIFFERENCE
An LLM-first agent puts a model inside a system of controls. A neuro-symbolic runtime gives executable business logic decision authority, with neural understanding and expression in the same computation.
01 / LLM-FIRST
02 / APOLLO-1
One neuro-symbolic design, not the only one. The second diagram is Apollo-1. Neural and symbolic parts can be combined in several ways, and where decision authority sits is the choice that matters here; Apollo-1 puts it in the program.
What changes in practice? If a refund requires confirmation, an LLM-first design relies on the surrounding controls to catch a proposal that skips it. In a neuro-symbolic design the symbolic logic at the center makes confirmation a condition of permitting the refund.
SIDE BY SIDE
Neither column is a verdict. They describe what each architecture guarantees, which is what should decide between them for a given task.
| Dimension | LLM agent | Neuro-symbolic runtime |
|---|---|---|
| Who decides the action | The model proposes; controls accept, reject or retry | The declared program decides; the model never overrides it |
| Policy enforcement | A guardrail must catch a proposal that breaks policy | A condition must hold before the action is permitted |
| Repeatability | Varies between runs; reduced by prompting, not removed | Same facts, version and state produce the same outcome |
| Arithmetic | Generated by the model or delegated to a tool call | Executed as formulas over established values |
| Explanation | The model writes an account of why it answered | The trace names the facts, rules and calls that ran |
| Changing behavior | Prompt changes, fine-tuning, or new guardrails | A reviewable edit to a versioned program |
| Running cost | Inference on every reasoning step | Rules and calculations on CPUs; focused language inference |
| Best suited to | Open-ended work where a wrong answer is cheap | Work with consequences, policies and figures that reconcile |
Comparison of architectural properties, not a benchmark. What any specific system achieves depends on its implementation, the task and the integrations around it.
COMMON QUESTIONS
The difference is where decision authority sits. An LLM agent has a model propose the next action, with orchestration, guardrails and a judge around it to catch bad proposals. A neuro-symbolic runtime gives an explicit program the authority to decide, and uses the model to interpret the request and express the result. One design checks the model after the fact; the other never lets the model make the call.
Guardrails catch what they were written to catch. If a refund requires confirmation, an LLM-first design relies on a surrounding control noticing a proposal that skipped it. In a neuro-symbolic design the confirmation is a condition of the refund being permitted at all, so there is no proposal to catch.
Neuro-symbolic, by construction. Given the same established facts, program version and state, symbolic rules and calculations return the same result every run. LLM output varies between runs, and prompting or temperature settings reduce that variance without removing it.
Neuro-symbolic systems move rules and arithmetic onto CPUs and keep the language model on interpretation and wording, which needs far fewer tokens than reasoning through a task repeatedly. An LLM agent that reasons over every step pays model inference for work that is not language work.
When the task is open-ended and the cost of a wrong answer is low: drafting, summarising, brainstorming, exploratory research and general assistance. Those tasks have no fixed policy to enforce and no figure that has to reconcile, which is where an LLM agent is strongest.
They already are. A neuro-symbolic system contains a language model; the question is not whether a model is present but whether it holds decision authority. The practical choice is which part of the system is allowed to determine an outcome that has a consequence.
THE TERM ITSELF
THE IMPLEMENTATION
PUT APOLLO-1 TO WORK
