Unlike a human expert, an LLM's probability estimates and conclusions can be drastically altered by simple rephrasing or irrelevant suggestions. This instability shows they are too easily "pushed around" and lack the coherent world model necessary for trustworthy, high-stakes decision support.

An ideal workflow separates probabilistic and deterministic tasks. Use an LLM agent for the creative front-end: helping users identify business constraints, research regulations, and formulate the problem. The agent then calls a dedicated mathematical optimization engine to generate a guaranteed, reliable, and explainable solution.

Unlike LLMs, which can hallucinate and behave unpredictably in novel situations, EBMs have an architecture designed to be constrained. A human can define a set of rules or constraints, and the EBM is forced to follow them, making it a more reliable choice for mission-critical systems like autonomous vehicles or financial trading.

Use LLMs to help define business problems, write code, and identify potential constraints. Then, hand off to a mathematical solver like Gurobi, which provides a mathematically guaranteed optimal solution that an LLM cannot, as it will never violate a hard constraint.

LLMs are technically non-deterministic systems designed to guess the next most probable word, not verify facts like a calculator. This inherent design means they will confidently produce incorrect information, making human verification indispensable for high-stakes business decisions.

Prompts are effective for guiding a model's probabilistic behavior but are too unreliable for enforcing critical business logic. Use deterministic software—like schema validation and finite state machines—to guarantee rules and prevent production failures.

For critical enterprise functions like financial modeling, 99.9% accuracy from a probabilistic LLM is unacceptable. Platforms like Salesforce's Agent Force 360 solve this by layering deterministic logic and guardrails on top of the AI, ensuring compliance and preventing costly errors where even a 0.1% failure rate is too high.

Stop aiming for perfect model compliance. Instead, accept that LLMs will fail to follow instructions 1-3% of the time. True system reliability comes from robustly handling this failure spike with fallbacks like heuristics, cached results, or human review queues.

Salesforce is reintroducing deterministic automation because its generative AI agents struggle with reliability, dropping instructions when given more than eight commands. This pullback signals current LLMs are not ready for high-stakes, consistent enterprise workflows.