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.
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Contrary to the vision of free-wheeling autonomous agents, most business automation relies on strict Standard Operating Procedures (SOPs). Products like OpenAI's Agent Builder succeed by providing deterministic, node-based workflows that enforce business logic, which is more valuable than pure autonomy.
Fully autonomous agents are not yet reliable for complex production use cases because accuracy collapses when chaining multiple probabilistic steps. Zapier's CEO recommends a hybrid "agentic workflow" approach: embed a single, decisive agent within an otherwise deterministic, structured workflow to ensure reliability while still leveraging LLM intelligence.
To ensure AI reliability, Salesforce builds environments that mimic enterprise CRM workflows, not game worlds. They use synthetic data and introduce corner cases like background noise, accents, or conflicting user requests to find and fix agent failure points before deployment, closing the "reality gap."
To reliably translate a natural language policy into formal logic, Amazon's system generates multiple translations using an LLM. It then employs a theorem prover to verify these translations are logically equivalent. Mismatches trigger a clarification loop with the user, ensuring the final specification is correct before checking an agent's work.
Training AI agents to execute multi-step business workflows demands a new data paradigm. Companies create reinforcement learning (RL) environments—mini world models of business processes—where agents learn by attempting tasks, a more advanced method than simple prompt-completion training (SFT/RLHF).
Unlike Claude Projects where the LLM decides how to use tools, Skills execute predefined scripts. This gives users precise control over data analysis and repeatable tasks, ensuring consistent, accurate results and overcoming the common issue of non-deterministic AI outputs.
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.
Fully autonomous AI agents are not yet viable in enterprises. Alloy Automation builds "semi-deterministic" agents that combine AI's reasoning with deterministic workflows, escalating to a human when confidence is low to ensure safety and compliance.
The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.
Salesforce's Chief AI Scientist explains that a true enterprise agent comprises four key parts: Memory (RAG), a Brain (reasoning engine), Actuators (API calls), and an Interface. A simple LLM is insufficient for enterprise tasks; the surrounding infrastructure provides the real functionality.