The push to move AI agents from pilot to production is creating a new kind of tension inside enterprises. The technology may look ready, but leaders still have to answer harder questions: What happens when the AI agent makes the wrong decision? Who sees it, and how fast? What does the human inherit when the handoff happens? And what damage is invisible until customers start leaving?
In a recent conversation, Tony Shen, Senior Product Manager at Amazon Connect Customer, and Jeremy Puent, Principal Solution Architect at Amazon Connect Customer, laid out a pragmatic view of what responsible deployment needs to look like. Their central point was simple: the fastest way to scale agentic AI is to build confidence first using testing, simulation, and observability that match real customer behavior.
Why the AI Agent Rush Breaks Trust Faster Than Leaders Expect
Puent put it plainly: “Poor customer experience equates to losing trust.” And the business cost of lost trust is quantifiable: According to Harvard Business Review’s 2025 Customer Retention Technology report, acquiring a new customer costs 5 to 25 times as much as retaining an existing one. Every customer interaction that erodes confidence has a downstream price tag that rarely shows up on the deployment dashboard.
He went on to argue that many organizations are still validating AI agents in ways that do not reflect production reality.
Manual testing can help early on, but it doesn’t scale to the number of scenarios an AI agent will face once it’s handling live customer interactions. “People testing AI agents today is largely a non-automated function,” Puent said. “They’ve got a human being sitting down, interacting with it, subjectively saying… it was good or bad.”
The bigger gap is realism. Desk-based roleplay can’t recreate the messiness of real calls - noise, interruptions, stress, mumbling, changing context mid-sentence. AI-powered simulation can replay and vary thousands of real-world interactions so teams can test broader coverage and tougher conditions without exposing live customers to failure.
“You’ve got to find ways to minimize the risk and test more scenarios, complete with confidence,” Puent said.
In other words, the rush is not just about speed. It is about the mismatch between how AI agents behave, and how many organizations still evaluate readiness.
Testing that doesn’t reflect the full range of real customer scenarios isn’t testing, it’s wishful thinking. And wishful thinking is how you discover your AI agent’s worst moments live, in front of customers who will remember.
AI Agents Are an Operational Change, Not a Technical Launch
Puent warned that when organizations treat deployment as a purely technical exercise, they may check the engineering boxes while missing the customer experience outcomes that actually matter.
“You might be missing the feedback loop… the things that lead to the customer experience,” he said. “Even if it passed all of the tests, but it's a miserable experience for the customer, you're doing the wrong thing.”
Shen focused on what changes inside the customer experience operation, when AI agents become decision-makers between IVR and a human advisor. In traditional flows, the customer chooses to reach a person. With an AI agent, the system makes decisions that shape the conversation before a human ever joins. “There's an AI agent in the middle, making individual decisions,” Shen said.
“On the human side, they need to know what decisions that AI agent made and have… observability… or else… they might be doing the duplicate things or might be confusing the customer.”
This is where many deployments stumble. Without visibility into what the AI decided and why, escalations become slower, more confusing, and significantly more costly. When your team has to piece together a broken AI interaction, handle times spike, and as Forrester’s January 2025 report, Improving CX Can Drive More Than $1B In Revenue, highlights, these operational friction points actively drain millions from the bottom line.
Systems designed to avoid human escalation will optimize for containment. Systems designed for intelligent and flexible AI (agentic and deterministic with a human handoff when needed) will optimize for resolution. The architectural choice determines which outcome your customers experience — and which one they remember.
The Cost Of Getting It Wrong Is a Trust Bill You Pay Later
Shen tied the risk to Amazon’s ‘earn trust’ leadership principle and connected it to a business reality CX leaders know well.
“Trust is hard to earn and easy to lose. Once the trust is lost, it is more expensive to earn back.”
He also pointed out that retaining existing customers is cheaper than winning new ones, which means failures that erode loyalty can become a compounding cost.
That compounding effect is more severe than most deployment teams expect: one bad AI interaction can drive churn from a customer who wasn’t planning to leave.
Lost trust is costly, and often impossible, to win back with the same customer. Retention is always cheaper than acquisition, so deployment isn’t just a product decision; it’s a retention decision.
For Shen, the answer is not fear. It is discipline. Enterprises need confidence that AI agents can handle real scenarios before customers are exposed to them.
That requires testing beyond the happy path.
“You got to let AI agent… behave in those edge cases,” Shen said. “My team and I, we're building those tools at Amazon Connect Customer to let you validate at scale before you deploy.”
Why Pre-Production Has To Be a Phased Discipline
Shen argued that skipping structured pre-production stages is a dangerous shortcut because it shifts uncertainty into the environment where mistakes are most visible and most expensive. Forrester’s 2025 Total Experience Score reinforces this, showing that brands suffering public CX failures face steep financial penalties in immediate customer churn and long-term recovery costs.
Pre-production testing isn’t just technical validation, it’s the basis of customer trust. Every scenario you don’t test before launch is one your customers will test for you, with their real experience at stake.
“When you're testing in production, it's just too risky,” he said. “You're exposing all the issues you haven't found out before… you really don't want the real customers to test your product.”
He described a phased approach, moving through environments such as dev, beta, gamma, and pre-production. Each stage exists to catch issues early, automate repeatable validation, and make rollback possible if changes introduce problems.
This approach allows you to slow down and then speed up. Resolving any issues means you go live quickly and smoothly, then adding more use cases also goes quickly and smoothly.
For AI agents, the requirements are fundamentally different than with deterministic automation — because agent behavior changes with context and language.
“AI agents, they’re not static,” Shen said. “They respond in context… and they’re non-deterministic.”
In other words, they’re probabilistic: they adapt to tone, phrasing, and context in real time, which is what makes them feel conversational. But it also means you won’t always get the exact same answer twice -introducing a class of risk traditional automation doesn’t.
That’s why “slowing down to speed up” is a strategy, not caution.
Shen also challenged the idea that skipping stages “saves time.” “It just moves the issue and the problems from testing to production,” he said.