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.

Stay Current with Your Certification

Salesforce certifications hold the most value when you keep them relevant. To keep your Agentforce Specialist certification current, complete this badge by the due date.

Interested in learning more about getting certified? Check out the Agentforce Specialist certification.

While anyone can earn this badge, it is designed for those who hold the Agentforce Specialist certification.

As part of the Salesforce Certification Program, you agree to the terms of the Salesforce Certification Program Agreement. Review the exam policies in the agreement and the Salesforce Certification Program Agreement and Code of Conduct article before you continue.

Agentforce Specialist Certification Overview

As an Agentforce Specialist, you deliver value when you help organizations build, deploy, and maintain reliable AI agents. Certified professionals design AI workflows, ground agents with relevant data, and manage the full agent lifecycle across development, testing, and production environments.

Certified Agentforce Specialists deliver value when they:

Build and customize AI agents for specific business needs.
Design AI workflows using prompts, actions, data, and automation.
Improve agent reliability, governance, and performance through testing and clear controls.
Monitor and improve agent behavior using analytics, observability, and operational insights.

In this badge, we highlight key updates you need to know for Summer ’26 to keep your certification current and your skills sharp.

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.

Design and Manage AI Workflows with Agentforce Grid

Agentforce Grid provides a spreadsheet-like workspace for designing, testing, managing, and updating AI workflows. Teams can manage workflows with multiple steps and conditions and combine Salesforce data, prompts, actions, and agents in a single workspace.

With Agentforce Grid, you can:

Design workflows without code.
Test workflows in a structured workspace.
Make bulk updates across many records.
Use run conditions to control when workflow steps execute.
Estimate usage costs with the Billing Calculator.

Run conditions help control when parts of a workflow run. For example, a prompt might run only when a customer meets specific criteria or when a previous step returns a certain result. This gives teams more control over how workflows behave.

The Billing Calculator helps organizations estimate workflow costs before deployment. Teams can review workflows, refine prompts and logic, test processes, and manage updates after 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.

Wrap Up

In this unit, you explored Summer ’26 updates that change how teams build, manage, and monitor AI agents. You learned how Agent Script, Agentforce Grid, and Agentforce Observability help organizations create more reliable workflows and better understand agent behavior after deployment.

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