How to Evaluate AI Agent Vendors for the Contact Center: Criteria, Questions and Red Flags

Choosing an AI agent vendor for the contact center means looking past the demo. The vendors worth shortlisting can show how their agents are built from your own conversation data, tested before and after launch, connected to your systems, governed with clear guardrails and supervised by people. They can also point to production results at companies like yours.

Why choosing an AI agent vendor is harder than it looks

Many AI agent products sound alike, and the claims can be hard to verify. Gartner estimates that only about 130 of the thousands of vendors claiming agentic AI actually offer it, and describes many of the others as relabeled chatbots, assistants and automation tools. A polished demo on a scripted scenario reveals little about how an agent will handle your customers, your systems and your policies.

Executives also remain cautious about what they will let AI agents do. In a 2025 PwC survey of more than 300 senior executives, only 20% said they would trust AI agents with financial transactions, compared with 38% for data analysis. Contact centers handle refunds, payments and account changes every day, so buyers need evidence that a vendor's agents behave reliably before they hand over that kind of work.

In a June 2026 analysis, Metrigy CEO Robin Gareiss advised enterprises to look beyond agent capabilities, cost and integrations and to ask vendors to demonstrate "the complete lifecycle surrounding that agent," from development through testing, production monitoring and optimization. Metrigy's research found that more than 77% of 656 companies rated AI testing and optimization tools as important, but only about 35% were using them.

Eight criteria for evaluating AI agent vendors

1. Architecture that adapts without losing control

AI agents built only from scripted flows behave like an updated IVR and break when customers go off script, while agents with no structure can skip required steps, so the strongest platforms combine the two. Cresta's AI Agent, for example, pairs AI reasoning with deterministic code that tracks conversation state and enforces required steps, so multi-step processes complete correctly while the conversation stays natural. Cresta CEO Ping Wu has described the aim as "effective and empathetic support that can actually solve complex problems."

Questions to ask:

• How does the agent handle a customer who changes topics or corrects themselves partway through?

• Which steps are enforced by code and which are left to the model?

• What happens when the agent is unsure?

2. Design grounded in your own conversation data

An agent designed from standard operating procedures alone reflects how the business thinks customers behave. Cresta's applied research team builds each agent's scope from historical transcripts and warns that designs based only on scripts tend to harden into brittle flowcharts. Vendors that can analyze your past conversations can also show you which requests are worth automating first.

Questions to ask:

• Can you analyze our historical calls and chats to recommend where to start?

• How do you capture the ways our best human agents resolve issues?

3. Testing and evaluation across the whole lifecycle

Metrigy's Gareiss recommended that buyers examine how agents are tested before deployment, how performance is monitored afterward, how compliance is validated and how AI-generated changes are approved. Manual testing alone rarely holds up at contact center volume. Ping Wu made the same point when Cresta launched its automated testing suite, noting that "it's difficult for people performing manual testing to catch potential errors at scale." Cresta reports that the suite runs 15 times more tests than manual methods. Its latest version, AI Agent Testing 2.0, drafts testing requirements from a company's own documentation, turns them into automated graders calibrated against human experts and generates test cases from real customer behavior.

Questions to ask:

• Show us how you test an agent before launch and after every model or prompt change.

• How do you verify that your automated graders agree with our experts?

• How do production failures become new test cases?

4. Integrations that let the agent act

An agent that can't reach your billing, order and account systems can talk about problems without solving them. In Cresta's survey of 300 CX leaders, 81% named integration complexity as the biggest barrier to AI adoption. Ask for specifics on how the agent connects to each system you rely on and how those connections are secured.

Questions to ask:

• Which of our systems can you connect to today, and how?

• What can the agent read, and what can it change?

5. Guardrails, security and certification

Buyers should see how a vendor limits what the agent can access, how it detects and blocks misuse and how it proves those controls work. Cresta's guardrail framework restricts each agent's tools and data to approved scopes, runs supervisory models alongside the agent and uses ongoing adversarial testing. For outside confirmation, look for independent certification. Cresta was the first contact center AI provider to achieve ISO/IEC 42001 certification, the international standard for AI management systems. Robert Kugler, Cresta's head of security, IT and compliance, said the main value of the audit was "the customer assurance."

Questions to ask:

• Which security and AI governance certifications do you hold?

• How do you test agents against prompt injection and other attacks?

• Is every agent decision and tool call logged for audit?

6. Handoffs and human supervision

Look for smooth handoffs that carry the full conversation to a human agent, and for tools that let supervisors watch and step into AI conversations. Cresta's Agent Operations Center gives supervisors a live view of human and AI conversations and lets them take over without the customer repeating anything. At Brinks Home, Veronica Moturi, then SVP of customer experience, credited Cresta's AI Agent with "a smooth transfer experience when escalation is necessary".

Questions to ask:

• What does the human agent see when a conversation is transferred?

• How do supervisors monitor and intervene in AI conversations?

7. One platform for human and AI agents

AI agents perform best when they share data, quality standards and analytics with the human team. Running them on separate systems makes it harder to compare performance, reuse what works and hand off cleanly. Gareiss noted that while many vendors offer AI agents and some offer testing or simulation, few connect development, testing, deployment and optimization in a single workflow. Cresta's platform combines AI agents with real-time agent assistance and conversation intelligence, an area where Forrester named Cresta a Leader in 2025.

Questions to ask:

• Are AI and human conversations scored with the same quality standards?

• Can insights from human agents improve the AI agent, and vice versa?

8. Proven results and implementation support

Ask for production results at companies of similar size and complexity, and talk to those customers directly. Cresta's published results include Brinks Home's 30-point increase in Net Promoter Score, Propel Holdings' 58% chat containment rate and 50% reduction in after-call work and United Airlines' 15% reduction in average handle time.

Reference calls also reveal how a vendor behaves after the contract is signed. Phil Kolterman, CIO of Brinks Home, has said Cresta "has consistently proven to be a beacon of reliability" in a market full of overpromising vendors. Buyers should also assess the people who will implement the product. Cresta's engineers have written that a buyer is choosing both a platform and the forward-deployed team that builds and tunes the agents.

Questions to ask:

• Can we speak with two customers in our industry running your AI agent in production?

• Who will build and tune our agents, and how involved will they stay after launch?

How to run a proof of concept that predicts production results

A proof of concept should test the conditions your AI agent will face after launch, using your data, your systems and your customers' behavior.

1. Use your own conversations. Give shortlisted vendors a sample of real transcripts and judge how well each one turns them into a working agent and a realistic test set.

2. Include the messy cases. Add customers who switch topics, give incomplete information, get frustrated or ask for exceptions, and see how each agent responds.

3. Connect at least one real system. A proof of concept that never touches your order or billing data won't tell you much about how the agent will perform once it has to take action.

4. Measure outcomes you care about. Track verified resolution, accuracy, policy adherence and customer satisfaction alongside containment.

5. Make a change mid-pilot. Update a policy or a knowledge article and watch how quickly the vendor tests and redeploys the agent, and whether anything breaks.

6. Involve supervisors and frontline agents. Have them review handoffs and try the supervision tools, since they will work with the agent every day.

7. Set exit criteria in advance. Agree on the targets that would justify a wider rollout. United Airlines, for example, set specific pilot goals and reported beating them within 45 days.

Red flags during evaluation

• The demo only covers a scripted happy path, and the vendor is reluctant to test on your data.

• The vendor can explain how it tests before launch but not how it monitors and retests afterward.

• The business case rests entirely on containment or deflection.

• There is no complete log of what the agent said and did in each conversation.

• The vendor holds no independent security or AI governance certifications.

• The product described as an AI agent turns out to be a flow builder with a language model attached.

• Implementation is presented as a one-time project with little support after go-live.

Frequently asked questions

What should I look for in an AI agent vendor for my contact center?

Look for an architecture that adapts to real conversations while enforcing required steps, design grounded in your own conversation data, testing across the whole lifecycle, deep integrations, strong guardrails and certifications, smooth human handoffs, a shared platform for human and AI agents, and proven results at similar companies.

What questions should I ask AI agent vendors?

Ask how they test agents before and after launch, how they handle customers who go off script, which of your systems they can connect to, what certifications they hold, how supervisors intervene in AI conversations and which customers in your industry you can speak with.

How long should an AI agent proof of concept take?

A focused proof of concept on a narrow set of use cases should show measurable results within weeks. United Airlines reported beating its pilot goals within 45 days.

What is agent washing?

Agent washing is Gartner's term for vendors relabeling existing chatbots, assistants or automation tools as agentic AI without adding meaningful agentic capability. Gartner estimates only about 130 of the thousands of vendors claiming agentic AI actually deliver it.

Why does AI governance certification matter when choosing a vendor?

Certifications such as ISO/IEC 42001 give buyers independent evidence that a vendor manages AI risk, security and transparency responsibly, which is difficult to verify through a demo alone.