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Maintain Your Agentforce Specialist Certification for Summer ’26

Maintain Your Agentforce Specialist Certification for Summer ’26

Learning Objectives

After completing this unit, you’ll be able to:

  • Describe how Agent Script improves control and governance in Agentforce Builder.
  • Explain how Agentforce Grid supports designing, testing, and managing AI workflows.
  • Identify key metrics and troubleshooting tools in Agentforce Observability.
  • Explain how organizations monitor and improve AI agent performance after deployment.

Build More Reliable Agents with New Agentforce Builder

The new Agentforce Builder changes how teams build and manage AI agents. Instead of relying only on natural language prompts, builders can now use Agent Script—a structured language that combines deterministic logic with large language model (LLM) reasoning. Deterministic logic follows defined paths and rules, such as conditions and transitions, while LLM reasoning adds flexibility when interpreting and responding to requests.

With Agent Script, teams can create more reliable and consistent agent behavior using logic like:

  • If/then conditions
  • Transitions between topics and actions
  • Defined workflow paths

This added control helps organizations build agents that are easier to test, govern, and manage across the business.

The redesigned Agentforce Builder also introduces several new tools that support development and troubleshooting.

Feature

Purpose

Canvas and Script views

Build visually or work directly in script.

Built-in AI assistance

Speed up agent development.

Validation checks

Identify issues before deployment.

Step-by-step trace previews

Review how the agent interprets instructions and responds.

Step-by-step trace previews are especially useful during testing. These traces show how an agent selected actions, followed instructions, and generated responses. This makes troubleshooting and refinement much easier before deployment.

These updates give teams more control and visibility during agent development. Organizations can build AI agents with more consistent behavior and better understand how agents behave before deployment.

Monitor Agent Performance with Agentforce Observability

As AI agents move into production environments, teams need better ways to monitor performance, identify issues, and improve results over time. Agentforce Observability helps teams monitor, understand, and troubleshoot agent behavior after deployment. Agent Analytics, part of Agentforce Observability, provides dashboards, metrics, and tracing tools that help teams measure performance and understand how agents behave during real interactions.

Organizations can now track several new metrics that measure response quality and overall agent effectiveness.

Metric

What It Measures

User Feedback

Positive and negative user responses

Task Resolution Rate

How often the agent successfully completes tasks

Instruction Adherence Rate

How closely the agent follows instructions

Average Agent Toxicity Score

Potentially harmful or inappropriate responses

Agent Analytics also uses a semantic data model based on data model objects (DMOs) that helps teams create custom reports and analyze agent activity across the organization. To troubleshoot issues, teams use a split view that combines Interaction Details and Trace Events. Administrators review how an agent responded, which actions it selected, and where issues occurred during an interaction.

These updates help organizations monitor agent performance, investigate issues more efficiently, and improve agent behavior after deployment. 




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