Developing AI Agents

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

Start in sandbox – Develop and test cost-effectively
Begin with MVP – Focus on core functionality first
Monitor performance – Track both technical metrics and business outcomes
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.