In 2023, the Chevrolet of Watsonville, Calif. chatbot offered one lucky customer the deal of a lifetime.
The large language model (LLM)-powered negotiator agreed to sell a mid-sized SUV for just $1, assuring the customer that the terms were legally binding with a firm “no takesies backsies.”
Fortunately for Chevrolet, the buyer never came to collect his deeply discounted Chevy Tahoe or test the legality of that chat transcript in court. But the exchange has gained notoriety in the press (Ed. Note: the source of this incident) and on social media as a cautionary tale about AI gone awry.
McKinsey estimates productivity gains of 30%–45% in customer service alone from GenAI. But as powerful as they are, GenAI and LLMs produce non-deterministic (and inherently so) outputs.
The key to realizing the AI technologies’ potential lies in trust, validation, and governance. Enterprises that pair AI innovation with safeguards for accuracy, compliance, and brand reputation will gain both speed and confidence: ensuring AI delivers its full promise without compromise. The enterprises that combine innovation with accountability will unlock the full promise of AI: faster service, safer operations, and stronger customer loyalty.
Enterprises now have the opportunity to achieve speed and safety by deploying AI with clear visibility and assurance of how it behaves in real-world conditions.
The challenge of achieving them, however, becomes even more complex as AI systems evolve from simple chatbots and voice bots within IVRs to fully autonomous agents. Unlike traditional rule-based bots that follow predetermined scripts, agentic AI systems can reason, plan, and take independent actions to achieve goals.
Autonomous AI–powered solutions go beyond responding to customer questions. They proactively analyze situations, make decisions, and take action with or without human intervention.
The enterprises that combine innovation with accountability will unlock the full promise of AI: faster service, safer operation, and stronger customer loyalty.
This marks a breakthrough in capability and efficiency, enabling smarter, faster, and more seamless customer experiences (CXs). But while it represents a significant leap in capability and efficiency, it also amplifies the risks exponentially.
In this article, we’ll explore both the promise and the risks of LLM-powered and agentic CX applications for proactively testing, validating, and governing AI interactions. Thus ensuring innovation moves fast, but with trust and control built in from day one.
A Recipe for Risk
Like the overachiever who raises their hand for every question, LLMs will always offer a quick, confident response: though their enthusiasm can sometimes outpace their accuracy. Research from Vectara has found that widely used LLMs like GPT, Llama, Gemini, and Claude can produce varying responses: with output quality shifting depending on the task and prompt.
In customer-facing AI (or agentic AI) use, this highlights the importance of building in the right assurance and guardrails, so organizations can confidently harness GenAI’s potential while ensuring accuracy and trust.
And for good reason. The kind of confident improvisation noted above is much too risky. A chatbot that fabricates billing policies, product information, or medical guidance can erode customer trust, trigger compliance violations, and open the door to reputational or financial harm.
AI misuse, where an AI agent says something inappropriate, dangerous, or offensive, can also severely damage a brand’s credibility and bottom line. LLMs do not inherently understand the regulatory and compliance boundaries that govern CX, such as GDPR and HIPAA.
One faulty decision from an AI agent can ripple across thousands of customers and lead to a wave of risk for highly regulated industries such as finance.
For example, instances have arisen where tax preparation software chatbots have advised users that they could break laws, such as withholding tips. This has drawn regulatory scrutiny and harmed consumers who relied on those unchecked AI responses.
With regulatory pressure intensifying, including the EU AI Act and the Federal Trade Commission’s AI guidelines, risks such as the overcollection of personal data or unsafe recommendations will increasingly expose brands to potential breaches and penalties.
CX is not a place for improv or guesswork. By the time a GenAI response goes wrong, the damage has been done, and the window for customer second chances is constantly shrinking.
Legacy Methods Won’t Future-proof CX
Accuracy, accountability, and oversight must come first. In CX, that means no AI application should ever interact with a customer until it has been fully validated across every channel.
The truth is, legacy testing methods — manual, siloed, or even rule-based automation — simply weren’t designed for today’s AI-driven systems. Traditional scripts and workflows can’t keep pace with the complexity and unpredictability of modern AI.
GenAI and agentic AI represent a new breed of intelligence. Unlike deterministic software, their behavior is dynamic; responses change each time a question is asked. This variability is what makes them powerful, but also why they demand an entirely new approach to testing and validation.
With agentic AI, the stakes rise further. It’s no longer enough to verify outcomes; we must also understand how and why the AI made its decisions. This transparency is critical for incident analysis, compliance, and maintaining customer trust.
The scope of this challenge is only expanding. Enterprises are deploying bots across every channel — web, messaging, in-app, and emerging AI-powered platforms — in dozens of languages and countless use cases. Manually testing every possible conversation path is simply impossible.
CX is not a place for improv or guesswork. By the time a Gen AI response goes wrong, the damage has been done.
The future of CX will be powered by AI, but it must also be built on trust. That trust comes from rigorous validation, modernized testing approaches, and a relentless focus on accuracy before innovation reaches the customer.
Call to Action: We must ensure that AI doesn’t just transform CX but elevates it. That means reimagining how we test, monitor, and govern AI systems. Enterprises that make AI accuracy and transparency a board-level priority will be the ones that earn lasting customer trust and ultimately lead in the new era of autonomous customer engagement.
Employing AI to Help
Quality assurance (QA) is evolving. Traditional functional testing and monitoring are no longer enough. Today, enterprises need CX assurance.
CX assurance is the continuous, automated validation of real-world customer interactions, integrating proactive risk mitigation, AI governance, and QA earlier in the development cycle. It represents a shift from reactive quality control to predictive, intelligence-driven assurance.
The next generation of this approach is agentic AI-led testing. By leveraging AI to independently analyze, execute, and continuously improve testing workflows, organizations can move faster while maintaining accuracy and reliability.
Using LLMs like ChatGPT and other GenAI tools, agentic testing can replicate a wide range of real-world scenarios with intelligence and flexibility. These systems engage with CX applications, learn and adapt to customer intent, and deliver intelligent, real-time insights. Thus ensuring enterprises can confidently deploy AI-driven experiences that meet customer expectations.
Testing AI-powered Interactions
As agentic AI-driven CX ecosystems become increasingly complex, businesses are investing in CX assurance: continuous, automated validation of real-world customer interactions.
The CX assurance market is evolving rapidly. Enterprises are moving beyond functional testing and monitoring toward proactive risk mitigation, AI governance, and earlier integration of CX assurance into development cycles.
Agentic AI-first testing proactively validates changes, reducing the operational risks that often come with application updates. It spots vulnerabilities and performance issues early, clearing the way for smoother, safer deployments.
Using LLMs like ChatGPT and other Gen AI tools, agentic testing can replicate a wide range of real-world scenarios with intelligence and flexibility.
With intelligent, self-healing scripts that adapt to workflow, API, and application changes, production stability stays intact. Here’s how.
1. AI-powered creation of test cases.
Agentic testing begins with creating detailed test cases that represent the tasks, workflows, and decision points the AI agent will encounter in real-world operations.
These test cases define success criteria, expected behaviors, and potential failure scenarios. By crafting comprehensive and realistic test cases, organizations ensure that the AI agent is evaluated against situations it is likely to face. These range from routine actions to complex, high-stakes decisions.
2. Dynamic verification and interaction.
Once test cases are prepared, agentic testing applies dynamic verification, which evaluates the AI agent’s responses against expected outcomes while allowing for acceptable variation.
Unlike traditional testing, which often relies on strict pass/fail criteria and is ill-equipped to handle nuanced real-world scenarios, dynamic verification accounts for the complexity and variability of live CX environments.
This approach captures the AI’s reasoning, adaptability, and contextual understanding. It identifies errors, inconsistencies, or unexpected behaviors without demanding exact matches, providing a far more realistic assessment of performance in dynamic settings.
3. Auto-adapting test scripts.
After verification, agentic testing leverages auto-adapting test scripts combined with continuous monitoring to automatically adjust to changes in CX applications, workflows, or interfaces.
Monitoring tracks the AI agent’s performance in real time, detecting anomalies or deviations from expected behavior, while auto-adapting scripts update test logic as needed.
This enables continuous improvement without requiring testers to manually adjust scripts for every change, allowing them to focus on strategic tasks, optimizations, and complex scenario design.
Together, auto-adapting scripts and monitoring ensure the AI agent remains reliable, effective, and aligned with business objectives in dynamic environments.
Final Thoughts
AI is advancing at an unprecedented pace, and agentic AI testing is evolving just as rapidly. As AI agents become more sophisticated and integrated into critical workflows, testing tools and methodologies must keep up with these new demands.
The future of agentic testing will be defined by creativity, flexibility, and the ability to manage the increasing complexity of CX systems powered by LLMs.
GenAI-powered CX and agentic AI have the potential to transform efficiency and engagement, but only if AI agents operate with accuracy, safety, and brand alignment, ensured through the right testing.
The “launch now, fix later” mindset is over. The winners in this new era will be the brands embedding trust frameworks into their AI from day one, and upgrading traditional testing into advanced AI governance platforms built for autonomous customer interactions.
The moment to evolve is now: before customers, regulators, or costly missteps make the decision for you.