For core business automations, agentic AIs that "guess" are expensive and unreliable. A superior approach uses tools that convert natural language into deterministic, code-like workflows, which run consistently and use AI only when necessary.

To achieve true deterministic AI, transition conditions must be implemented as explicit code with deterministic semantics (e.g., a probability threshold). Relying on natural language instructions in prompts to guide an LLM is merely a simulation of control, not an enforceable structural guarantee.

Don't give LLMs full control. Use deterministic code for core logic, validation, and enforcing rules. Delegate only tasks requiring flexibility or understanding of unstructured input to the LLM, treating it as a specialized component, not the entire system.

Relying on prompt engineering for safety is insufficient and easily bypassed. The expert consensus is to build safeguards directly into the system's architecture. Architectural controls are immutable during runtime, whereas prompt-level controls can be manipulated or overridden by clever user inputs.

Tools like LangGraph support state machine patterns, but don't guarantee them. The critical shift is an engineering discipline where orchestration logic is explicitly designed and bounded by developers, rather than being improvised by the LLM at runtime.

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.

Relying solely on natural language prompts like 'always do this' is unreliable for enterprise AI. LLMs struggle with deterministic logic. Salesforce developed 'AgentForce Script,' a dedicated language to enforce rules and ensure consistent, repeatable performance for critical business workflows, blending it with LLM reasoning.

A prompted instruction like "never do X" is merely a probabilistic suggestion to an AI model and can fail. For critical rules, use 'hooks'—deterministic code that fires on specific events. This provides a guarantee of enforcement for actions that must always or never happen, a reliability that prose-based prompts cannot match.

To deploy LLMs in high-stakes environments like finance, combine them with deterministic checks. For example, use a traditional algorithm to calculate cash flow and only surface the LLM's answer if it falls within an acceptable range. This prevents hallucinations and ensures reliability.