Buyer's guide
Contact centre AI readiness and testing: a practical guide for Genesys Cloud
AI in the contact centre succeeds or fails on preparation and testing more than on the choice of model. This guide covers how to tell whether an organisation is ready, what is different about testing AI compared with scripted flows, what to test before customers meet it, the tools Genesys Cloud CX provides, and how to keep testing once it is live.
In short
- Readiness rests on six things: understood contact reasons, governed knowledge, safe integrations, data protection, people and a telephony model that supports the route chosen.
- AI does not give the same answer twice in the same words, so it is tested with many real examples and judged on outcomes, not exact responses.
- Test understanding, actions, knowledge, guardrails, hand-offs and, for voice, the call itself: accents, noise, interruptions and latency.
- Testing does not stop at launch: models, knowledge and customers change, so every significant change is retested and live conversations are reviewed.
01
Are you ready? Six areas to check
Readiness is not a maturity score; it is whether the foundations a particular journey needs are in place. An organisation can be ready for a copilot long before it is ready for an agentic agent that changes customer records. We assess each candidate journey against six areas, and the gaps we find usually become the first phase of work, because they would undermine any AI placed on top of them.
| Area | Signs you are ready | Signs you are not yet |
|---|---|---|
| Contact reasons | Reasons, volumes, handle times and outcomes are known per journey | Wrap-up codes are inconsistent or too broad to tell journeys apart |
| Knowledge | Content is current, owned and written for customers | Agents rely on memory, old documents or each other |
| Integration | The systems a journey needs have APIs and data actions you can govern | Resolving the contact needs screen-scraping or manual workarounds |
| Data protection and risk | A data protection impact assessment route, owners and guardrail sign-off exist | No one owns AI risk, or it is decided after the pilot |
| People | Owners for design, knowledge and AI quality; agents involved from the start | AI is a project with no owner once it goes live |
| Platform and telephony | The chosen route is supported on your telephony model and regions | The route depends on features not available in your configuration |
02
Why testing AI is different
A scripted flow is deterministic: the same input takes the same path every time, so a test either passes or fails. AI is not. A virtual agent or agentic agent may phrase the same answer differently on each attempt, choose a different but valid sequence of questions, or occasionally do something unexpected. Testing therefore moves from checking exact responses to judging outcomes across many attempts: did the customer get the right result, did the agent use the right tools with the right data, did it stay within its guardrails, and did it hand over when it should?
That needs more test cases than a scripted flow, written in customers' own words rather than the designer's. The best source is real transcripts and recordings, anonymised, covering the common phrasings, the unusual ones and the difficult cases. A good test set grows over the life of the service, because every failure found in live conversations becomes a test case that protects against the same failure returning.
03
What to test before customers meet it
We group AI testing into seven areas. Not every journey needs all of them in depth, but every journey needs each one considered and the decision recorded, so that a gap in testing is a deliberate choice with an owner rather than an oversight found by a customer.
| Area | What to check |
|---|---|
| Understanding | Real customer phrasings, including vague, indirect and multi-part requests, are understood correctly |
| Actions and tools | The right tools are called with the right data; errors, timeouts and empty results are handled safely |
| Knowledge | Answers match current approved content, and the agent says so when it does not know |
| Guardrails and misuse | Out-of-scope topics are declined, and attempts to override instructions or extract data fail |
| Hand-off | Customers can reach a person at any point, and context arrives with the agent |
| Voice and the call itself | Accents, background noise, interruptions, silence, latency, keypad input and secure payment flows |
| Volume and resilience | Behaviour under load, and a fallback when a provider or integration is slow or unavailable |
04
Testing tools in Genesys Cloud, and testing from outside
Genesys Cloud CX provides tools for each stage. An agentic virtual agent can be tried in the AI Studio preview widget before it is connected to a flow, to test its role and guidelines, data actions, knowledge, start and exit behaviour and guardrails, on a digital or voice channel. Architect's bot flow simulator tests digital bot flows, and flow execution history and replay mode show what happened in a given interaction: the sequence of tool calls, knowledge lookups, guardrail events, latency and status, and whether the interaction was contained, handed off or escalated, with the exit reason. Genesys describes these as helping determine why an interaction succeeded, failed or was handed off.
Tools inside the platform test the design. They cannot fully test what a caller experiences: the carrier route, the number dialled, the IVR in front of the bot, speech recognition on a real line, and the transfer to a queue and an agent. We always finish with real calls placed from outside the platform, the way customers call, across the numbers and routes in scope, with results recorded against the test plan. Testing then becomes evidence, which matters for the business case, for change control and for any regulator or auditor who asks how the AI was assured.
- Build the test set from anonymised real transcripts and recordings
- Test the agent in the AI Studio preview widget as you design it
- Test the flow in Architect, and use replay to diagnose failures
- Place real calls from outside the platform across every number and route in scope
- Record results against the plan and agree go-live criteria in advance
05
Guardrails and misuse
Customers will try things the designers did not expect, and a few will try to make the AI misbehave. The OWASP Top 10 for large language model applications lists prompt injection, where crafted input changes the model's behaviour, as its first risk. Genesys guardrails for agentic virtual agents include rules that detect attempts to override the agent's instructions, but the boundaries are yours to set and to test: what the agent must not discuss, what it must never do, what data it must never reveal, and what it does when a request is ambiguous. Test each rule directly and indirectly, in different wordings, and keep the results with the guardrail definitions so the next change can be checked against them.
06
After go-live: testing never stops
An AI service changes even when no one touches its flows. Customers change how they ask, products and policies change the knowledge, and models change underneath. Genesys versions the agent model behind agentic virtual agents and offers an opt-in period to test a new version before it becomes the default, which is the moment to rerun the full test set rather than discover differences in live traffic. Every change to instructions, tools, knowledge or guardrails deserves the same treatment, released through the same change control as the rest of the contact centre.
Live conversations are the richest test data there is. Review a sample regularly, prioritising hand-offs, repeat contacts and low satisfaction; trace each failure to its cause in the tools, knowledge or instructions; fix it; and add it to the test set. QVCCS is a Genesys referral partner, not a reseller, and testing is where we spend much of our effort, because it is the difference between AI that impresses in a demonstration and AI that customers can rely on.
Questions
Common questions
How many test cases does an AI agent need?
More than a scripted flow, and it depends on the journey. Cover every outcome the journey can reach, the common ways customers ask, the unusual and difficult cases, and each guardrail, and repeat key cases several times because AI responses vary. Grow the set from live conversations after launch.
Can we test AI automatically?
Partly. Test sets can be run repeatedly and outcomes checked automatically where they are clear, such as whether the right tool was called or the right hand-off occurred. Judging whether an answer was accurate, appropriate and well expressed still needs people, at least for a sample, especially after significant changes.
What should our go-live criteria be?
Agree them before testing starts, with operations, compliance and the business owner. Typical criteria cover correct outcomes across the test set, no guardrail failures, every hand-off working with context, acceptable latency on real calls, and a tested fallback. The thresholds are yours to set, but they should be written down and met, not negotiated on launch day.
Is a pilot enough testing?
No. A pilot with real customers is valuable evidence, but it should follow structured testing, not replace it, because customers should not be the first to find a guardrail failure or a broken hand-off. Run the pilot on a limited journey, with monitoring and a quick route back to people.
Sources
- Test and troubleshoot agentic virtual agentshelp.genesys.cloud
- Preview an agentic virtual agenthelp.genesys.cloud
- Agentic virtual agents overviewhelp.genesys.cloud
- About AI Guideshelp.genesys.cloud
- OWASP Top 10 for LLM Applications: prompt injectiongenai.owasp.org
- Guidance on AI and data protection (ICO)ico.org.uk