AI-powered customer interactions are here, and enterprises are racing to deploy AI agents that can resolve inquiries, automate support, and deliver measurable efficiency improvements. But with great power comes great responsibility.
Imagine a customer calling an airline to ask if they can bring their pet on board. The AI agent confidently responds, “Yes, KronosJet welcomes pets!” But when the passenger arrives at the gate, they find out “welcoming” meant in the cargo hold—and a reservation was required weeks ago. Chaos erupts at the gate. A social media backlash. Refunds, reputation damage, and possibly regulatory scrutiny follow.
What went wrong? In a word: testing. The AI wasn’t fully tested to ensure accuracy, compliance, and consistency. Even the best Large Language Models (LLMs) are known to produce misleading information.
Entrusting Customer Experience to AI
Today’s genAI-native agents can automate millions of interactions, reducing costs and improving response times. But powerful Large Language Models (LLMs) are still unpredictable. They generate responses dynamically, which means they can hallucinate, go off-script, or misinterpret customer intent in ways that a scripted chatbot never would—one 2024 Cornell study found that AI-generated text is only hallucination-free about 35% of the time.
This creates a paradox: companies want the efficiency and scalability of AI, but they can’t afford to lose control over customer experience. Businesses need assurance: a way to test, refine, and validate AI performance before it ever interacts with customers, and even after.
A Smarter Approach: Simulations + Evaluations
The key to deploying AI agents responsibly isn’t waiting for failures to surface. It’s preventing them altogether. That’s where processes like Simulations and Evaluations come in. AI agents must be field-tested using real-world circumstances.
“By incorporating simulation-based testing into their security strategy,” observes Amy Stapleton, Senior Analyst at Opus Research, “organizations can gain a more comprehensive understanding of their GenAI application’s behavior and identify areas for improvement before deploying to customers for the first time.”
LLMs can be used to create two new AI agent types: AI agents that act like customers, as well AI agents to act as evaluators, or judges, of the corresponding synthetic conversations. This enables a two-step process:
What does this look like in practice?
Simulations
Simulations let businesses test AI agents in real-world conditions before deployment—running thousands of scenarios to surface risks through synthetic conversations with AI-simulated customers.
For example, a hotel chain using an AI concierge can simulate thousands of guest interactions at a scale human QA teams could never recreate manually. The simulations help identify potential risks before the AI agent is deployed, ensuring a smoother launch.
These tests can evaluate a wide range of interactions, such as:
- Answering FAQs – “What time is check-in?” “Does the hotel have a gym?“
- Executing a process – Booking a spa appointment or arranging a late checkout.
- Handling customer-specific requests – “When do I need to renew my loyalty status?“
- Routing customers correctly – Directing a guest to the right department for billing vs. room service.
Evaluations
Evaluations automate AI performance assessments, ensuring agents meet accuracy, compliance, and brand standards—mixing AI-driven rules, a process known as “AI-led Evaluation” or “LLM-as-a-Judge” — and deterministic rules.
Building on the hotel concierge example, evaluations can quickly verify that the AI agent is:
- Providing all required information – Confirming check-in times, cancellation policies, or loyalty program benefits.
- Following brand guidelines – Using the correct tone and approved terminology (e.g., “suite” vs. “executive room”).
- Executing processes correctly – Calling the right API at the right time when confirming room availability for late checkout.
Testing may uncover a flaw—such as the AI agent approving late checkouts without checking real-time occupancy, causing overbookings. Evaluations catch those issues early and can also continue to test AI accuracy after launch, ensuring the AI agent remains reliable as real-world conditions evolve.
Without these steps, spotting AI agent failures is like searching for a needle in a haystack—manual review simply doesn’t scale.
As Michael Reichardt, Product Manager at insurance company BarmeniaGothaer observed, “LLM projects require a number of simulations and thorough reviews to get the best results. Looking at each simulation manually would take a lot of time, but Parloa’s new evaluation feature makes this process much more efficient and saves valuable time – time that I can focus on further development. A real game-changer!”
Best Practices for Customer-Ready AI Agents
One common reaction to AI-led evaluations is, “Isn’t this just AI judging AI? What if the AI evaluation is wrong?” This does somewhat miss the point. Unlike manual review, which is slow and inconsistent, AI-led evaluations can audit thousands of interactions instantly, identifying issues faster than any human reviewer. The addition of deterministic checks ensures clear, rule-based quality control, reinforcing AI accountability and preventing doubling down on errors.
Of course, companies should always follow best practices when defining an LLM-as-a-judge evaluation rule, to ensure AI agents are deployment-ready. These include:
Define precise pass/fail criteria – Make instructions clear and unambiguous.
Avoid overly complex evaluation tasks – If humans struggle to assess it, AI evaluators will too.
Review AI-generated explanations – Understand why AI evaluators scored a pass or fail.
Use hybrid evaluation approaches – Combine AI-based assessments with deterministic rules.
Baking AI Governance into the Process
Of course, the work doesn’t stop at deployment. Even after an AI agent goes live, continuous evaluation is essential to monitor performance and catch new risks as they emerge — something that is coming from Parloa later this year.
As AI adoption shifts from early adopters to global enterprises, those who invest in rigorous AI governance will innovate faster, safer, and with greater confidence.
That’s why Simulations and Evaluations are core to Parloa AMP. Parloa is leading innovation in AI safety, ensuring that:
AI interactions are transparent and traceable.
Sensitive customer data remains protected.
AI systems meet the highest reliability standards.
These two critical steps give businesses the ability to deploy AI responsibly and maintain control over all their most important customer interactions.
Interested in learning more?
- Download a complimentary copy of Opus Research’s latest report on genAI trust and safety
- Contact us to learn more about simulations and evaluations for your AI agents
