Simulation
Using Simulation in raia Academy to Test AI Agents
How Simulation works
Overview
Simulation in raia Academy allows you to create realistic test conversations for your AI Agents before deploying them in a live environment. This feature lets you design scenarios that mimic real-world situations, run them against your agents, and then review the results in raia Copilot for human feedback and performance tuning.
Why Simulation is Important
Testing your AI Agents in realistic scenarios before launch helps:
Identify weaknesses in understanding or response accuracy.
Validate conversation flow and ensure it aligns with your intended use case.
Reduce live environment errors by catching issues early.
Improve training data through targeted feedback.
Measure real-world readiness without impacting real customers.
How It Works
Create a Simulation
Go to the Simulation section in raia Academy.
Click Add Simulation.
Choose between:
Single Scenario – Test one specific situation.
Bulk Scenarios – Upload or create multiple situations for broader testing.
Define the Scenario
Write a description of the test situation as if you were describing it to a human tester.
Example for a support agent:
"A customer is contacting support to change their payment method due to a failed credit card."
"Customer reports their recent order arrived damaged and they want to return the item."
Select the Agent
Pick the AI Agent you want to test from your available list.
Choose Internal Type (if applicable)
Select the type of internal test (e.g., support inquiry, sales request, technical troubleshooting).
Run the Simulation
Click Create Simulation.
The simulation will generate AI-to-AI conversations based on your scenario.
These conversations are sent to raia Copilot for review.
Reviewing & Providing Feedback in raia Copilot
Once a simulation is complete:
Open raia Copilot and navigate to the simulation conversations.
Review the AI Agent’s responses:
Was the answer factually correct?
Was the tone appropriate?
Did the conversation resolve the scenario successfully?
Rate the responses (e.g., thumbs up/down, 1–5 stars).
Provide human feedback:
Suggest corrections or improvements.
Flag missing information or incorrect logic.
Feedback is automatically saved to improve the agent’s future performance.
Why Human Feedback from Simulations Matters
Accuracy – Ensures the agent’s answers are correct and useful.
Consistency – Standardizes responses across similar scenarios.
Adaptability – Helps the AI learn how to handle new or unexpected queries.
Reduced Hallucinations – Directs the AI away from making up information.
Continuous Improvement – Every simulation review makes the agent smarter.
Best Practices
Simulate common customer interactions to cover high-frequency scenarios.
Include edge cases (rare or tricky situations) to test resilience.
Run simulations after major knowledge updates to verify accuracy.
Involve multiple reviewers to gather diverse feedback perspectives.
Re-run failed scenarios after training to confirm fixes worked.
Example Workflow
You update your Support Agent’s knowledge base.
You create three test scenarios:
Payment method change due to failed credit card.
Product return request for a damaged item.
Customer requesting shipping status update.
Run the simulations → AI generates test conversations.
Support team reviews the conversations in raia Copilot.
They rate, correct, and add feedback.
The agent is retrained on the updated feedback data.
You re-run the simulations to confirm improvement.
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