Some useful best practices for Agentforce implementations

As an AI-First Agentforce Implementation Partner, we’ve seen firsthand how a well-structured approach can drastically improve agent reliability on Salesforce. Too often, agents become unpredictable when instructions are scattered or poorly defined. Here, we share our best practices, honed from real-world experience, that ensure your Agentforce agents operate accurately and consistently, even under complex conditions.

Why structure matters

Building agents that handle dynamic inputs, execute actions accurately, and adapt to various edge cases is no small feat. When done right, you’ll achieve:

• Increased reliability: Agents remain consistent across different types of user interactions.

• Higher performance: Proper scoping and organized workflows eliminate unnecessary delays and reduce error rates.

• Improved maintainability: A logical structure and robust testing make it easier to update agents without breaking existing functionality.

Our approach

We developed a standardized framework combining thorough instructions, consistent formatting, and comprehensive testing:

1. Consolidated Workflow Design

   • Keep each topic’s instructions in a single cohesive block.

   • Define the role of the agent explicitly (e.g., “sales agent,” “support assistant”) so Salesforce knows the agent’s expected behavior.

2. Process-Oriented Instructions

   • Lay out the workflow in ordered steps that the agent can follow or revisit as needed (not just a linear sequence).

   • Use exact action names and call them with clear verbs (e.g., “use createCase to open a new case”).

3. Input/Output Formatting

   • Specify input formats (like MM/DD/YYYY for dates) to minimize confusion.

   • Keep the output structure consistent for easy downstream parsing or logging.

4. Guardrails for Data Validity

   • Prompt users again if they provide contradictory or invalid data (e.g., a date that doesn’t exist or a weekend date for a weekday-only policy).

   • Emphasize specific conditionals so the agent can accurately handle user-supplied information.

5. Comprehensive Testing

   • Classification Tests: Ensure correct topic classification, even with varied user phrasings.

   • Boundary & Edge-Case Tests: Evaluate how the agent handles incomplete information, invalid dates, or large records.

   • Integration Flow Tests: Check end-to-end conversations and confirm proper data pass-through among multiple actions.

Implementation example

Below is a minimal example illustrating a single topic for creating a Salesforce support case. Notice how we keep instructions in one place and specify clear input formats.

Topic: Create Support Case

Instructions:
1. Classify the topic if user mentions "create a case" or "open a ticket".
2. Prompt user for issue details:
   - Issue description (text)
   - Issue start date (date, MM/DD/YYYY)
3. Validate inputs:
   - If the date is invalid, prompt again.
4. use createCase to open a case in Salesforce
   - Input: {
       "description": userIssueDescription,
       "startDate": userIssueStartDate (MM/DD/YYYY)
     }
5. Confirm successful case creation to the user

Some notes on testing, guardrails, and edge cases

• User claims: If a user tries to provide contradictory data (e.g., a future date for a past incident), revalidate.

• Data volumes: Test how your agent handles large record sets or concurrency limits, especially when chunking data.

• Custom constraints: Watch out for unique fields or integrations that require specialized handling.

By adopting these structured best practices, our clients have reported:

• Reduction in agent misclassifications.

• Drastically lower error rates when processing bulk or complex data.

• Faster refinement cycles, thanks to consolidated testing and clear instruction sets.

Final thoughts

A robust Agentforce development strategy starts with clear, cohesive instructions, strict input/output definitions, and focused testing. These practices create agents that perform reliably, reduce development overhead, and allow you to scale confidently.

Whether you’re new to building agents on Salesforce or looking to refine a mature implementation, these best practices set the foundation for building trust and momentum on your agent implementations within your larger AI Transformation initiatives.

If you have questions or need guidance tailoring these insights to your specific use case, feel free to reach out. Our team has extensive experience delivering high-performing Agentforce solutions, and we’d be happy to help you do the same!