Developing AI Agents: A Practical Guide

Why AI Agent Development is Different

Building AI agents requires an iterative, hands-on approach rather than traditional waterfall planning. Unlike conventional software projects, you can’t fully design an agent in documents before building – you need to prototype, test, and refine continuously.

Key Principles:

• Build while planning – Start developing in a sandbox as you define requirements
• Test early and often – Experiment with different approaches to see what works
• Embrace iteration – Let real-world testing guide your development

Designing the User Experience

Take a user-centered approach to ensure your agent delivers value:

• Internal vs. external users:
  • Employees typically need complex, varied assistance
  • Customers often have more predictable, transactional needs
• Channel considerations:
  • Where will users interact with the agent? (Web, mobile, internal systems)
  • How should the agent present itself? (Tone, personality, capabilities)

Protip: Test different UX approaches in sandbox before finalizing channel configurations.

Technical Implementation Guide

1. Data Strategy

• Identify all required data sources
• Ensure data quality and accessibility
• Map data flows for agent decision-making

2. Channel & Routing Configuration

• Define all interaction channels (web, app, messaging platforms)
• Plan escalation paths to human agents when:
  • Policy/regulations require human oversight
  • Complex issues exceed agent capabilities
  • Security/authentication is needed
• Integrate with existing routing solutions (like Omni-Channel)

3. Security Framework

For Employee-Facing Agents:

• Inherits logged-in user’s Salesforce permissions
• Respects field-level security and sharing settings

For Customer-Facing Agents:

• Implement authentication for sensitive actions
• Consider tiered access (public info vs. account-specific help)

Agent User Permissions:

• Create dedicated agent user profile
• Required permissions typically include:
  • Agentforce Service Agent permission sets
  • Data Cloud User access
  • Platform—Flow User privileges
  • Appropriate object CRUD permissions
  • Associated Einstein Agent license

Testing Tip: Always debug flows running as the agent user to catch permission issues.

Salesforce Environment Considerations

Requirements Checklist

• Lightning Experience enabled
• Einstein Generative AI activated
• Data Cloud configured
• Sandbox environment available for development

Integration Planning

• Audit existing automations (flows, Apex) that could be repurposed
• Evaluate current Einstein Bots for potential agent conversion
• Plan consumption monitoring (Agentforce uses usage-based billing)

Implementation Best Practices

1. Start in sandbox – Develop and test cost-effectively
2. Begin with MVP – Focus on core functionality first
3. Monitor performance – Track both technical metrics and business outcomes
4. Iterate based on data – Continuously improve agent capabilities

Remember: Successful AI agent development balances careful planning with agile experimentation. The most effective agents evolve through real-world testing and refinement.