AI Agent Management

eeV.ai's AI agents are intelligent virtual assistants that handle customer interactions across multiple channels with configurable behavior and learning capabilities.

Agent Overview

What are AI Agents?

AI agents are sophisticated chatbots powered by advanced language models that can:

• Understand customer inquiries in natural language
• Provide accurate responses based on your knowledge base
• Learn from interactions to improve over time
• Escalate complex issues to human agents
• Maintain conversation context and history

Key Capabilities

• Multi-Channel Support: Work across email, chat, WhatsApp, and voice
• Contextual Understanding: Maintain conversation context
• Knowledge Integration: Access your entire knowledge base
• Sentiment Analysis: Detect customer emotions and respond appropriately
• Escalation Management: Know when to involve human agents

Agent Architecture

AI Provider Integration

eeV.ai supports multiple AI providers:

Supported Providers

• OpenAI GPT: GPT-3.5 and GPT-4 models
• Anthropic Claude: Claude AI models
• Google Gemini: Gemini Pro and Ultra
• Cohere: Command and Generate models
• Mistral AI: Mistral 7B and larger models
• Perplexity: Real-time web-connected AI

Provider Selection

• Performance Comparison: Different models for different use cases
• Cost Optimization: Balance performance with cost
• Latency Requirements: Choose based on response time needs
• Language Support: Multi-language capabilities

Agent Configuration

Core Settings

• Agent Name: Unique identifier for the agent
• Description: Purpose and scope of the agent
• AI Provider: Selected language model
• Model Version: Specific model variant
• Temperature: Response creativity level (0-1)

Behavioral Parameters

• Confidence Threshold: Minimum confidence for responses (0-100%)
• Response Time: Target response speed
• Personality: Tone and communication style
• Escalation Rules: When to transfer to humans

Agent Training

Knowledge Base Integration

Training Data Sources

• Knowledge Base Articles: Primary training content
• FAQ Collections: Common questions and answers
• Historical Conversations: Past interaction data
• Product Documentation: Technical specifications
• Policy Documents: Company policies and procedures

Training Process

• Content Ingestion: Process knowledge base content
• Semantic Understanding: Build concept relationships
• Response Generation: Create appropriate responses
• Quality Validation: Test response accuracy

Continuous Learning

Learning Mechanisms

• Interaction Feedback: Learn from customer ratings
• Human Corrections: Incorporate agent corrections
• Conversation Analysis: Analyze successful interactions
• Performance Metrics: Optimize based on KPIs

Improvement Strategies

• Regular Retraining: Periodic model updates
• Content Updates: Refresh training data
• Feedback Integration: Incorporate user feedback
• A/B Testing: Test different configurations

Agent Performance

Key Metrics

Response Quality

• Accuracy Rate: Percentage of correct responses
• Relevance Score: How well responses match queries
• Completeness: Whether responses fully address questions
• Clarity: How understandable responses are

Efficiency Metrics

• Response Time: Average time to respond
• Resolution Rate: Percentage of issues resolved
• Escalation Rate: How often human intervention needed
• Customer Satisfaction: User ratings and feedback

Usage Statistics

• Conversation Volume: Number of interactions handled
• Channel Distribution: Usage across different channels
• Peak Hours: Busiest interaction times
• Topic Analysis: Most common inquiry types

Performance Optimization

Configuration Tuning

• Confidence Adjustment: Optimize confidence thresholds
• Response Tuning: Improve response quality
• Speed Optimization: Reduce response latency
• Accuracy Enhancement: Increase correct responses

Training Improvements

• Content Expansion: Add more training material
• Quality Enhancement: Improve training data quality
• Specialization: Focus on specific domains
• Multi-modal Training: Include various content types

Agent Deployment

Channel Assignment

Multi-Channel Deployment

• Email Integration: Handle email inquiries
• Live Chat: Real-time website conversations
• WhatsApp: Messaging platform support
• Voice Calls: Phone conversation handling

Channel-Specific Configuration

• Response Formats: Adapt to channel requirements
• Media Support: Handle images, documents, etc.
• Character Limits: Respect platform constraints
• Feature Utilization: Use channel-specific features

Deployment Strategies

Gradual Rollout

• Pilot Testing: Start with limited deployment
• Performance Monitoring: Track initial performance
• Feedback Collection: Gather user feedback
• Full Deployment: Scale to all channels

A/B Testing

• Configuration Variants: Test different settings
• Performance Comparison: Compare agent versions
• User Experience: Measure user satisfaction
• Optimization: Implement best-performing configuration

Agent Management

Monitoring and Control

Real-time Monitoring

• Live Conversations: Monitor active interactions
• Performance Dashboard: Real-time metrics
• Alert System: Notifications for issues
• Intervention Capability: Human takeover when needed

Quality Assurance

• Response Review: Regular response quality checks
• Conversation Audits: Detailed interaction analysis
• Customer Feedback: User satisfaction tracking
• Continuous Improvement: Ongoing optimization

Agent Lifecycle

Development Phase

• Initial Configuration: Basic agent setup
• Training: Knowledge base integration
• Testing: Validation and quality assurance
• Optimization: Performance tuning

Production Phase

• Deployment: Live agent activation
• Monitoring: Ongoing performance tracking
• Maintenance: Regular updates and improvements
• Evolution: Continuous learning and adaptation