Agentforce Agent Types
Choose the right agent types for your financial services use cases
Service Agent
Customer-facing AI for support and inquiries
Common Use Cases
- Account balance inquiries
- Transaction history requests
- Document retrieval
- Appointment scheduling
- Basic account updates
Handle 70% of routine inquiries, freeing advisors for high-value interactions
Sales Development Agent
Lead qualification and outreach automation
Common Use Cases
- Lead scoring and prioritization
- Initial outreach campaigns
- Meeting scheduling
- Opportunity qualification
- Follow-up automation
Increase qualified lead volume by 3x while reducing SDR costs
Operations Agent
Back-office process automation
Common Use Cases
- Data validation and enrichment
- Document processing
- Compliance checks
- Workflow orchestration
- Report generation
Reduce operational costs by 40% through intelligent automation
Analytics Agent
Data analysis and insights generation
Common Use Cases
- Portfolio performance analysis
- Risk assessment
- Client segmentation
- Trend identification
- Predictive forecasting
Enable data-driven decisions with 24/7 analytical capabilities
5-Phase Implementation Methodology
Proven framework for successful Agentforce deployments
Use Case Identification
Key Activities
- Map current manual processes
- Identify automation opportunities
- Assess compliance requirements
- Define success metrics
- Prioritize use cases by ROI
- Create implementation roadmap
Deliverables
- Use case documentation
- ROI analysis
- Compliance assessment
- Implementation timeline
Data & Knowledge Preparation
Key Activities
- Audit existing knowledge bases
- Structure unstructured content
- Create training datasets
- Establish data quality standards
- Build knowledge graph
- Define agent permissions
Deliverables
- Knowledge base architecture
- Training data sets
- Security model
- Data quality rules
Agent Configuration
Key Activities
- Configure agent personas
- Define conversation flows
- Set up action triggers
- Implement guardrails
- Configure escalation rules
- Build testing framework
Deliverables
- Agent configuration docs
- Conversation flows
- Guardrail documentation
- Test scripts
Training & Testing
Key Activities
- Agent training sessions
- User acceptance testing
- Performance optimization
- Compliance validation
- Security testing
- Load testing
Deliverables
- Training materials
- UAT results
- Performance benchmarks
- Compliance certification
Deployment & Monitoring
Key Activities
- Phased rollout execution
- User training delivery
- Performance monitoring setup
- Feedback collection system
- Continuous improvement process
- Hypercare support
Deliverables
- Deployment checklist
- Monitoring dashboards
- User training certification
- Support documentation
Compliance & Regulatory Considerations
Ensure your AI agents meet financial services regulatory requirements
Data Privacy (Regulation S-P)
- Encrypt all PII in agent conversations
- Implement data retention policies
- Configure audit logging
- Restrict agent access to sensitive data
Communication Surveillance (FINRA 3110)
- Capture all agent-client interactions
- Enable review workflows
- Implement keyword flagging
- Archive conversations for 7 years
Suitability & Best Interest (Reg BI)
- Configure agents to collect suitability info
- Prevent unsuitable recommendations
- Escalate complex scenarios
- Document all interactions
Books & Records (SEC 17a-4)
- Store agent logs in WORM storage
- Implement legal hold capability
- Enable eDiscovery
- Maintain conversation audit trail
Implementation Best Practices
Proven strategies for successful Agentforce deployments
Agent Design
- Start with narrow, well-defined use cases
- Design clear escalation paths to humans
- Use persona-based agent configurations
- Implement confidence thresholds
- Test extensively before deployment
Knowledge Management
- Maintain single source of truth
- Version control all knowledge articles
- Regular content audits and updates
- Structured metadata tagging
- Clear content ownership
Guardrails & Safety
- Define hard stops for sensitive topics
- Implement toxicity detection
- Block PII disclosure
- Rate limit agent actions
- Log all guardrail triggers
Continuous Improvement
- Monitor agent performance metrics
- Analyze conversation failures
- Collect user feedback
- A/B test conversation flows
- Regular retraining cycles
Key Performance Metrics
Measure success with these industry-standard benchmarks
Containment Rate
Percentage of inquiries resolved without human intervention
Average Handle Time
Time from initiation to resolution for contained inquiries
Customer Satisfaction
User rating of agent interactions
Escalation Rate
Percentage of conversations escalated to human agents
First Contact Resolution
Inquiries resolved in single interaction
Accuracy Rate
Correctness of agent responses validated