Introducing Simulations: End Manual Bottlenecks in AI Agent Testing

Written By:

Brian Howarth

Published

Aug 26, 2025

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Last updated

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5 min read

AI agents can hold natural, dynamic conversations with customers at scale, across dozens of different use cases.

And while that flexibility is what makes them so impactful, it also introduces risk and complexity to the development process. AI Agents conversations can branch off in dozens of different directions—some predictable, some not at all.

When it comes to testing, manual QA can no longer keep up with the complexity, non-determinism, and scale of enterprise AI agent deployments. It’s too time consuming, and leaves too many blind spots in scenario coverage.

You can’t role-play every scenario. And with manual testing alone, you have to start from scratch every time you make an update to the agent prompt. And because of the non-deterministic nature of conversations, if your first live test goes well, that doesn’t assure that it’ll test well again the next time through.

So, it’s either… Spend weeks manually testing as many cases as possible, or find a happy path quickly (say, 70-80% scenario coverage), launch, and let real customers be the ones to discover conversational flaws.

AI Simulations are Regal’s answer to that tradeoff.

Simulations let you auto-generate and simultaneously run dozens of scenario-aware end-to-end conversations, simulating hours of conversations in seconds. The AI keeps regenerating test cases as you update your agent prompt, so every future iteration can be validated instantly without rebuilding from scratch.

All in all, your iteration cycles shrink, you cover more edge cases, and you harden your agents against risk before a single live call is made.

Simulations: The End of Manual Bottlenecks in AI Agent Testing

With Simulation Testing, Regal uses an LLM to simulate the customer side of a call, interacting with your AI agent end-to-end.

How does it work? Mini-prompts are used to define the specific scenario being tested, and the personality and traits of the contact on-call.

Regal uses AI to auto-generate test cases in bulk, but you can also manually configure and delete tests as desired.

After you run tests, you’re left with the simulated transcript for proof.

So, overall, with Simulations:

You auto-generate suites of test cases in bulk
Run end-to-end tests in bulk, in seconds
Get real evidence of each conversation, with invoked actions called out throughout the transcript
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With this, you can quickly pinpoint where an agent mishandled an objection or question, potentially misfired on retrieval, missed a required action, or disregarded a step in the conversation.

And, by simulating these test cases before an agent is live, no real customers are exposed to faulty interactions.

Automate Test Creation, Grounded in Your Prompts and Logic

LLMs can’t generate meaningful test cases on their own. They can analyze language, but they don’t know your objections, your workflows, or your branded style.

With Regal, you can auto-generate test cases with AI.

Regal’s AI-generated test cases are built using the contents of your existing agent prompt—so they’re completely rooted in your voice settings, your objection handling, your guardrails, your branded style, and your knowledge bases.

In the Test Cases page, simply select “Get Me Started,” and the AI will generate 10 spanning use cases in seconds:

Once generated, you can run these test cases individually, or dozens at a time.

Test cases can easily be regenerated and rerun as your agents evolve, as well. So coverage always stays current, and the cases always stay relevant to the most up-to-date prompt.

Test Based on Real-World Outcomes

In the midst of building your AI agent, you’ll utilize Test Logic and Test Audio. These interfaces enable you to have a conversation with your AI Agent in-platform (either via live chat or audio) to test basic prompt logic, common objection handling, pronunciation, and how the voice sounds, quickly rewinding and iterating as needed.

Once you’ve confirmed baseline conversation paths with Test Logic and Test Audio, you’ll move to Simulations for end-to-end scenario testing—running cases in bulk, and re-running test cases when needed to uncover reliability issues in LLM responses or prompt design.

Each test case contains two prompts.

First, a Contact Prompt, which includes:

The call scenario: What’s actually happening on the call (e.g. “Customer confirms their upcoming appointment for a bathroom remodel consultation.”
The customer persona: The contact’s behavior and disposition (enthusiastic, organized, inquisitive, financially curious, etc.)
Customer attributes: The contact’s name, to be used directly in the conversation simulation.
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Then, the Success Criteria—a plain language description of what the correct outcome looks like (e.g. “Agent addresses concerns, explains pricing clearly, and attempts to schedule an appointment”).

Measure Success by Outcomes

The power of Simulation Testing isn’t just in running one-off conversations, it’s in refining them.

Every test transcript helps surface where an AI Agent succeeds and where it falls short. From there, you can quickly adjust prompts, knowledge bases, or guardrails, regenerate your test cases, and re-run them until the agent consistently meets expectations.

That refinement loop gives enterprises full coverage across the core dimensions of AI Agent performance, allowing you to measure and refine:

Conversational Logic: Verify that the agent follows the designed task flow under diverse scenarios, handles objections/questions consistently, and integrates retrieval-augmented generation (RAG) responses when needed.
Custom Actions: See “Action invocations” in transcripts to confirm that scheduling, transfers, and other custom actions will trigger at the right time.
Parallel Scenarios: Run multiple test cases against the same agent prompt simultaneously, ensuring that changes hold up across different conversation paths.
Reliability: Run the same test multiple times with the same or different agent personas to validate that the agent is consistently performing as expected across your customer base.
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That coverage extends into more nuanced observability of a conversation as well:

Information delivery: Does the agent provide the right details (pricing, policy coverage, appointment availability) every time?
How the agent addresses the contact: Does it acknowledge urgency, safely decline to answer, and maintain brand voice across variations of the same request?
Handling silence: Does the agent wait appropriately or re-prompt the customer when there’s no response?
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Speed Up AI Agent Iteration Cycles

Testing is a critical part of the agent development process because it greatly impacts both the speed of deployment, and the performance of the AI.

Enterprises win by iterating faster than the competition. Simulations are an accelerator.

The impact on speed is threefold:

Parallel validation: Dozens of scenarios can be re-run instantly to confirm that today’s changes didn’t break yesterday’s flows. And, every time you adjust a prompt or KB, you can re-generate test cases in just a few minutes to match the updates.
Evidence-based edits: Every failure traces back to a specific prompt, so fixes are more precise and immediate, not trial-and-error.
Future-proofing: Simulation Testing lays the foundation for continuous integration in AI agent QA, where pass/fail validation is as automated and reliable as code testing.
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That level of precision lets you make immediate targeted fixes that dramatically lower risk, without sitting through hours of role-play calls.

De-Risk Critical Journeys at Scale

In enterprise contact centers, the cost of a missed “branch” could mean lost revenue or broken customer trust.

AI agents are handling more complex, dynamic conversations than ever (lead qualification, scheduling, inbound support). These conversations don’t typically fail on the “happy path.”

They fail in the edge cases:

Multi-turn requests: A patient says, “I need to book my appointment and also update my address.”
Out-of-scope requests: Customer asks a scheduling agent, “Can you also check on my billing statement?”
Emotional cues: A parent says tearfully, “I can’t afford this treatment,” and the agent must respond thoughtfully before moving to next options.
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Simulations provide the safety net that manual QA can’t. Instead of roleplaying calls one by one, you validate entire conversation flows end-to-end, across hundreds of branches, edge cases, and personas—all in bulk.

This equates to scalable coverage across different conversation types, but also scalable coverage across every potential path within every conversation type.

See How Simulations Drive Specific Action

Simulation Testing comes to life when you see how it exposes breakdowns in real conversation flows:

Prompting for Clearer Pricing FAQ Handling (Insurance)

The Test Case: Customer Requests More Information on Pricing

Scenario: Customer calls about pricing for an auto policy.
Persona: Financially cautious, inquisitive.
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Result: Simulation shows the AI agent responds vaguely: “Pricing depends on your plan.” The agent doesn’t explain the structure or try to move the conversation forward. You want your agent to be more informative and proactive.

Action Taken: You update the prompt to instruct the agent to refer to your “Policy Pricing” knowledge base and explain pricing tiers clearly, and then offer to explain coverage tiers for more context on pricing.

Re-run Result: The updated simulation shows the agent now responds with: “Pricing can be as low as $85 per month, but varies depending on your personal details, vehicle, driving habits and history, and the plan you’re approved for. Would you like me to walk you through our coverage options?” From here, the agent dives into specific policy rates.

Adding Custom Actions for Cleaner Callback Scheduling (Home Services)

The Test Case: Callback Scheduling Request

Scenario: Customer requests to schedule a callback for future assistance.
Persona: Busy, prefers organized communication.
Result: Simulation shows the AI agent responds with: “I can call you back later today,” but doesn’t confirm a time or invoke the “end_call” action properly.
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Action Taken: You refine the task prompt to more explicitly confirm with the contact what time would work best for them, and then add in custom actions to gather_date/time and then end the call once the time is confirmed.

Re-run Result: The updated simulation shows the agent now responds with: “I totally understand! Is there a day and time that works best to call back?”

Upon the contact responding with a day and time, the AI confirms the time with custom action “gather_date/time” and ends the call.

Improving Guardrails for Inquiries on Unsupported Services (Healthcare)

The Test Case: Unsupported Service Handling

Scenario: Patient asks about physical therapy services.
Persona: Eager, concerned.
Result: Simulation shows the AI Agent attempts to answer incorrectly: “Yes, we can help with that,” even though the practice doesn’t offer physical therapy.
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Action Taken: You update the guardrail and prompt to ensure unsupported services are addressed clearly and offer a transfer path when necessary.

Re-run Result: The updated simulation shows the agent now responds with: “I want to be clear that our clinic doesn’t provide physical therapy services. Would you like me to transfer you to a representative who can recommend trusted providers in your area?”

Faster, Safer Enterprise Iteration

In enterprise environments, scaling up isn’t just about taking more calls. It’s about consistently driving results across every possible branch.

Testing with AI Simulations compresses iteration cycles while reducing risk. With automated test case generation, simulated conversations tailored to your prompt and customer based, and comprehensive visibility into the agent’s performance on the call, you can move faster without exposing customers to broken flows, compliance gaps, or off-brand dialogue.

You can ship updates quickly, prove correctness at scale, and keep agents aligned with your brand—all while running continuous regression testing that safeguards every critical flow before a single customer hears a word.

If you’re looking to generate AI test coverage at scale, talk with one of our experts today to see it live.

Frequently Asked Questions

Founded in 2020, Regal is an enterprise voice AI agent platform for contact centers. Regal helps businesses build, deploy, and manage autonomous AI agents across sales, support, and operations teams.

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Today we are excited to announce that based on the success our customers are seeing using Regal.io, we have raised $38.5 million in Series A funding led by Emergence Capital to continue to invest in our teams and products.

Announcing our Integration with mParticle

Our integration with mParticle enables you to create a unified customer profile and connect other first party customer data.

What is a Journey Builder?: A Complete Guide

Say goodbye to batch processing, complicated SQL queries or custom engineering to decide who to call. Journey builders help B2C create more timely, relevant and personalized outreach to customers.

Goodbye Regal Voice, Hello Regal.io

Friends and supporters, We are excited to announce our updated branding and website. It better captures what we stand for.

Announcing our Integration with Twilio Segment

We are excited to announce our integration with Twilio Segment, allowing you to seamlessly connect your first-party customer data with Regal.io and 200+ other integrated tools.

Milestone: $1 Billion Revenue Driven for Our Customers

We are thrilled to announce that Regal.io has driven over 20M conversations between our customers and their end users, leading to $1 billion in revenue driven to date.

The RAG Playbook: Structuring Scalable Knowledge Bases for Reliable AI Agents

Learn how to structure a knowledge base that keeps AI Agents accurate and on-script. Discover why human-style KBs fail, and apply best practices like single-topic chunking, concrete instructions, and explicit outcomes to reduce hallucinations and scale reliable RAG performance.

How to Use SIP to Integrate Regal Voice AI Agents with your Contact Center Software

Learn how SIP integration enables enterprises to connect Regal Voice AI Agents to existing CCaaS platforms, pass call context in real time, and deploy voice AI without infrastructure changes.

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