The excitement surrounding generative AI in customer experience (CX) is undeniable. From highly responsive chatbots to autonomous “proxy” systems that rebook flights and process refunds, the potential for scale and efficiency is transformative. However, as we move from scripted deterministic logic to probabilistic large-scale language models (LLMs), traditional testing playbooks are no longer sufficient.
In a world where AI agents can give different (but correct) answers to the same prompt every time, or suddenly become confused, how can you ensure that your AI agents maintain your brand, respond within company policies, and avoid other issues? As Metrigy reveals in its CX research, the new risks associated with using AI agents are driving many companies to We now have tools and processes in place that aim to ensure that agents perform as expected before deployment and maintain that performance after deployment.
Here are seven top tips for ensuring your AI agents perform well before and after go-live, based on insights shared in conversations with many industry experts.
1. Adopt a “simulation and scale” mindset
Traditional testing relies on “if-this-then-that” logic, but AI requires a shift in thinking. To gain true confidence, testers need to simulate a wide variety of operational scenarios. This includes testing how agents handle different customer personalities, regional accents, and complex intents (sometimes multiple intents in a single interaction). By running dozens of automated simulations for any given scenario, you can evaluate an amount of behavior that human testing simply cannot match.
2. Implement a “red team” before deployment
Before you go live, put yourself in your enemy’s shoes. Red teaming, essentially ethical hacking of AI, is a critical pre-deployment step. Don’t rely solely on automated models. Actively abusing the system or attempting to trick or breach guardrails. Identifying these vulnerabilities in a sandbox environment can help you avoid reputation-damaging mistakes in the real world.
3. Treat AI as a “lower-level employee”
When you’re starting out, it’s safest to consider your AI agents as junior employees. You won’t be able to give new employees complete autonomy from day one, and the same is true here. Define narrow areas of responsibility and give AI agents access to only the specific data and backend APIs they need to perform their jobs. Once an agent proves its reliability through consistent performance, it can expand its capabilities and be promoted incrementally.
4. Use AI to judge AI
Leverage technology itself to extend quality assurance. Use multiple models to monitor each other. For example, as mentioned above, it’s a smart choice to use a red team model designed to override the primary agent’s response or find defects. In production, use a “reflection” layer where agents evaluate their plans before execution, or use a separate monitoring agent that flags potential hallucinations or errors for human review.
5. Stay informed about people
In this latter respect, automation does not mean eliminating humans. That means rearranging them. Successful strategies often involve AI running the conversation, with a human supervisor assisting behind the scenes via chat. When AI is unsure about a high-stakes decision or edge policy decision, it can consult a human without the customer knowing. This hybrid approach ensures that complex or sensitive issues always receive the necessary due diligence.
6. Establish definitive guardrails
LLM is probabilistic, but business rules must be deterministic. If you want to allow your AI agent to offer discounts, set a hard, non-negotiable threshold (for example, no more than 15%). These hard guardrails provide a safety net to ensure that AI agents never compromise revenue or regulatory compliance.
7. Define AI-specific success metrics and measure results
While traditional contact center metrics like average handle time and customer satisfaction (CSAT) remain important, AI requires more nuanced key performance indicators. For example, we measure “true containment” rather than simple containment. Did the agent do that? actually Did the problem get resolved? Or did the customer get frustrated and hang up? Additionally, track how often your agents hit guardrails or deviate from their intentions as a key indicator of health.
The real trick for many organizations is to avoid falling into the AI demo trap. These tips will help.
