Picture a virtual agent that passes every scripted test in your regression suite. A week after launch, it issues a credit to the wrong account, updates a Salesforce record it was never meant to touch, and tells the customer everything went smoothly. Nothing in the test results warned you, because every test checked what the agent said. None of them checked what it decided to do.

That gap is the reason agentic AI testing has become its own discipline. Contact centers have moved from rule-based IVRs to conversational bots, then to generative assistants, and now to agents that plan, call tools, and execute workflows on their own. Most QA programs still assume a system behaves the same way every time and follows a scripted path. Agentic systems do neither, and validating a decision takes a different model than validating a response.

Why Traditional QA Falls Short for Agentic AI Testing

AI innovation is outpacing AI validation. Organizations are putting systems into production that they cannot fully validate, at a pace that limits their ability to catch failures before customers do. The consequences are showing up in the data. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls.

For contact center and CX leaders, that risk compounds in three places at once:

• Compliance requirements around AI-driven customer interactions keep tightening
• Customer-facing failures are more visible, and more expensive, than ever
• Financial liability grows when an autonomous agent takes the wrong action on a customer's behalf

The frustrating part is that most QA teams are working hard. Their tools were simply built to measure outputs, and agentic systems fail in their reasoning, their tool use, and their actions. Frameworks such as the NIST AI Risk Management Framework make the same point: trustworthy AI depends on measuring how a system behaves in context, and customers deserve an agent they can trust to act for them.

Kenway has spent years helping contact centers modernize how they test and scale automation, from  Contact Center Solutions work to the API-level approach described in A in our latest whitepaper, A Unified Framework for Agentic AI Quality Assurance. Kenway put Cyara Botium's agentic AI testing capabilities to work across three virtual assistants, each at a different level of AI maturity: a foundational chatbot handling onboarding, an enterprise conversational and generative AI assistant focused on billing and payments, and an agentic assistant orchestrating Salesforce-based business workflows.

What Agentic AI Testing Revealed Across Three Virtual Assistants

Running objective-based agentic AI testing across all three assistants confirmed a set of risks that scripted QA does not detect:

• Hallucinated actions. The agent executed incorrect decisions, and those failures moved through entire workflows before anyone noticed.
• Decision pathway drift. The same objective produced different execution paths across runs, which makes repeatability a KPI to measure rather than a property to assume.
• Evaluator risk. LLM-based evaluation introduced its own variability and needed active governance to separate reliable results from false positives.
• Configuration sensitivity. Misconfigured execution modes, timeouts, and session parameters produced systematically wrong results with no visible error.

There was good news, too. The same objectives and virtual tester personas built for agentic AI testing also ran correctly against the non-agentic assistant. Teams can modernize their QA program without discarding existing investments or running parallel programs for legacy and agentic systems and set up a AI Governance Framework.

A Four-Phase Plan for Agentic AI Testing

The shift at the center of agentic AI testing is simple to state. Instead of defining what a system should say, you define what it should accomplish. Those objectives become the backbone of a program that tests decision pathways, confirms which actions were taken and what data was accessed, and uses persona-driven simulation to expose the variability real customers bring. Organizations can build that program in four phases, each delivering value on its own.

Phase 1: Foundation

Define objectives, risk tiers, and a governance model so business, CX, and QA stakeholders agree on what a correct outcome looks like before the first test runs.

Phase 2: Enablement

Configure the testing platform and build reusable persona and objective libraries. This is where configuration discipline pays off, since small setting errors can quietly skew every result.

Phase 3: Scale

Integrate agentic AI testing into CI/CD pipelines and extend coverage across channels, so validation keeps pace with release frequency.

Phase 4: Optimization

Use feedback loops and KPI-driven improvement to sustain trust after launch. Botium's confidence scoring helps here by routing low-confidence results to human review and flagging high-confidence failures for investigation before they advance. Monitoring for drift in production keeps the trust you built during testing from eroding once real traffic arrives.

The Cost of Waiting, and the Payoff of Getting It Right

Organizations that keep testing agentic systems with scripted, output-only methods will keep finding their failures in production. Every hallucinated action that reaches a customer means an escalation, a remediation effort, and a little less confidence in the next release. Over time, the QA backlog grows, releases slow down, and the business case for AI starts to look like one of those canceled projects.

Teams that adopt objective-based agentic AI testing see a different picture:

• Fewer production defects, because decision pathways are validated earlier in the SDLC
• Better containment, because persona-driven validation produces more predictable outcomes across releases
• Faster deployment, because continuous validation in the pipeline shortens release cycles without adding risk
• Lower rework costs, because defects are caught before they turn into technical debt

The result is a contact center where leaders can launch new AI capabilities with confidence, and customers can trust the agent acting on their behalf.

Get the agentic AI testing executive overview and full technical whitepaper from Kenway to see the complete framework and methodology.