What are AI Agent Simulations?
AI agent simulations are controlled environments where AI agents can be tested, evaluated, and refined through various scenarios and interactions.Scenarios
Scenarios are structured test definitions used for simulating voice AI and chatbots to unearth potential issues and edge cases. They define the specific conditions, inputs, and expected behaviors that your AI agents will encounter during testing. For simulating voice AI and chatbots, scenarios help identify:- Communication breakdowns
- Unexpected user behaviors
- Edge cases that may cause failures
- Performance under various conditions
Example: Sales Agent Selling Insurance
Consider a sales agent designed to sell insurance policies. Here are possible scenarios it might encounter:| Scenario Type | Description | Example |
|---|---|---|
| Interested Customer | Customer actively seeking insurance | ”I need car insurance for my new vehicle” |
| Price-Sensitive Customer | Customer focused on cost | ”What’s your cheapest option?” |
| Skeptical Customer | Customer questioning the need | ”Why do I need insurance?” |
| Comparison Shopper | Customer comparing with competitors | ”Your competitor offers the same for less” |
| Technical Questions | Customer asking detailed policy questions | ”What’s covered under comprehensive?” |
| Objection Handling | Customer raising concerns | ”Insurance is too expensive” |
| Urgent Need | Customer needs immediate coverage | ”I need coverage starting today” |
Types of Scenarios
Scenarios can be created in four different ways, each offering unique advantages for testing your AI agents:-
Workflow Builder: Visual graph-based scenario creation with automatic generation capabilities
- Manual Building: Create conversation flows using Conversation, End Call, and Transfer Call nodes
- Automatic Generation: AI-powered creation of scenarios with personas, situations, and outcomes
- Best For: Comprehensive testing with diverse conversation paths
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Import Datasets: Structured data scenarios using CSV, JSON, or Excel files
- Pre-defined Data: Upload existing customer profiles and test cases
- Synthetic Generation: Create artificial datasets with specified parameters
- Best For: Testing against known customer profiles and data patterns
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Upload Script: Script-based scenarios for specific edge cases and targeted testing
- Conversation Scripts: Define exact dialogue flows between customer and agent
- Automatic Graph Building: Scripts are automatically converted to graph structures
- Best For: Testing specific interactions, compliance scenarios, and corner cases
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Call / Chat SOP: Standard Operating Procedure scenarios for structured workflows
- Process Definition: Define standardized procedures for customer interactions
- Automatic Processing: SOPs are converted to testable conversation flows
- Best For: Ensuring consistency, compliance, and training standardization
Agent Definition
This is your agent that you want to test - the AI voice agent or chatbot that will be evaluated through simulations.Definition
Each agent on Future AGI represents your unique AI agent. These are conceptual entities used to organize and configure your Voice Agents with specific behaviors, capabilities, and constraints within the simulation environment. Agent definitions include:- Agent Configuration: Core settings and behavior parameters
- Contact Information: Phone numbers and communication channels
- Voice Settings: Voice provider, tone, and conversation parameters
- Capabilities: What the agent can and cannot do
- Business Logic: Rules and workflows the agent follows