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A Practical Guide to Reliable AI Agents That Work

Balance innovation with integrity to build enterprise-grade agents you can trust.

Introduction

Business leaders have trust issues with LLMs, and for good reason. Most AI models are too creative for high-stakes business tasks. They skip steps, ignore rules, and hallucinate facts. This lack of reliability keeps companies from scaling their AI investments. The research backs this up: Gartner predicts companies will cancel 40% of agentic AI projects by 2027 and cites reliability as the top barrier. Meanwhile, Anthropic's latest Economic Index confirms that AI success rates drop as task complexity increases.

LLMs alone aren't reliable engines for agents. While they’re great at sounding confident and professional, they often produce unpredictable results in complex or critical workflows. We observed this challenge during our own first agent deployments. Even with detailed prompts, agents didn’t always behave as intended.

As Agentforce Product Director Aron Kale observes, this lack of trust and control often traps enterprise workflows in the “shallow end of the pool,” limited to simple FAQs and low-stakes work.

That’s why we transformed our platform to give you total control over how your agents act. We introduced Agentforce Script and hybrid reasoning to give your agents the right blend of probabilistic and deterministic outcomes.

Instead of prompt tuning and hoping the agent follows your “first, always, never”-style instructions, you can now inject those rules into a script that gets them right every time. Users can easily dial up or dial down deterministic control over their agents.

In this guide, learn how hybrid reasoning changes the way we should all think about building powerful, reliable agents that blend the creativity of LLMs with the predictability needed for the enterprise.

A diagram illustrating the Agentforce hybrid reasoning flow for a customer exchange request. Agent Script logic runs in a ‘Before’ step (Get order, Check loyalty tier, Search inventory), followed by ‘During’ logic to offer the exchange or troubleshoot during the LLM's Reasoning. Finally, Agent Script logic executes a deterministic ‘After’ step (Share tracking link, Send SMS confirmation) to ensure all rules are fulfilled.

With Agentforce Script, natural language instructions like “always,” “before,” or “never” are converted into actual, executable, deterministic logic. When this customer requests an exchange, Agentforce Script runs a set of required actions that surround the LLM's creative reasoning, ensuring all business rules are followed in the correct order.