Introduction to Salesforce Agent Script: Build Predictable AI Agents

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In my latest blog, I introduce Salesforce Agent Script and explain how it helps teams build AI agents that are more predictable, controlled, and aligned with business processes.

Large language models are flexible, but relying only on natural-language instructions can sometimes produce inconsistent results. Agent Script addresses this challenge by combining the conversational capabilities of an LLM with structured business logic.

The blog explains how developers can use conditions, variables, actions, topics, and transitions to control how an Agentforce agent behaves. Agent Script supports familiar logic such as if/else conditions, value comparisons, sequential actions, and checks for missing data.

I also cover its core structure, including system settings, configuration, global variables, languages, connections, and topic blocks. The start_agent topic acts as the entry point for each conversation and routes the request to the appropriate topic.

Agent Script uses a readable, indentation-based format and the @ symbol to reference actions, topics, variables, and action outputs. This makes complex workflows easier to understand, debug, and maintain.

The blog includes an appointment-scheduling example that demonstrates how an agent can collect details, check availability, book an appointment, suggest alternatives, and transition to a rescheduling topic.

The key takeaway is that Agent Script brings greater consistency and control to Agentforce while preserving a natural conversational experience.