This is the second blog in a series about Provar TrustAI. If you missed the first installment, Why AI Agents Require a Different Testing Strategy, you can find it on the Provar Blog. 

In our last blog, we explored why AI agents require a different testing strategy. The next question is straightforward: what does that strategy actually look like?

Provar TrustAI adapts proven software testing principles for AI agents, combining structured evaluations, repeatable test scenarios, and consistent measurement into a single testing workflow.

In today’s blog, we’ll walk through the key components of that workflow.

Sessions

AI testing begins with understanding how an AI agent behaves in real conversations.

Provar TrustAI connects directly to your AI agent and captures every conversation, tool call, and sub-agent handoff in one place. Production conversations can help teams build meaningful coverage, but they aren’t required to get started. A small set of representative conversations is enough to begin creating repeatable test scenarios.

Scenarios, Personas, and Goals 

Representative conversations and even written business policies become structured, repeatable test cases.

Each scenario can be evaluated across multiple personas and goals. Different users bring different expectations, communication styles, and objectives to the same conversation. A frustrated customer, a power user exploring edge cases, and a non-native speaker might all approach the same task differently. Broader coverage helps teams evaluate how an AI agent performs across a wider range of real-world situations.

Evaluators

An AI agent can produce a polished response and still take the wrong action. 

TrustAI combines deterministic checks with LLM-based evaluation. Deterministic checks verify what the agent actually did, including selecting the right tools, passing the correct parameters, routing work appropriately, and creating or updating records as expected. LLM judges then evaluate the quality of the response itself.

TrustAI also measures how closely those LLM judges align with human-reviewed outcomes. That gives teams greater visibility into the reliability of the evaluations themselves. 

Evaluations, Requirements, and Regression

Structured evaluations can be executed at scale, with every result linked back to the business requirement or policy it supports.

As agents, models, and policies change, teams can rerun the same evaluations as part of their regression testing process and monitor how pass rates evolve over time. The same regression discipline applied to traditional software releases can also support AI agent development.

Test Data Management

Reliable testing depends on reliable data.

Every evaluation runs against a controlled test environment where users, scenarios, and supporting data remain consistent. Controlled test data helps teams determine whether a failure reflects agent behavior rather than changing test conditions.

Built to Fit Existing Workflows

TrustAI is designed to fit naturally into existing development workflows.

Teams can interact with TrustAI through its in-product AI assistant or from tools they already use, including Claude, ChatGPT, Codex, and Cursor through MCP. Organizations can adopt structured AI testing without changing how their teams already work.

Future capabilities may include compliance coverage and continuous production monitoring, extending TrustAI beyond pre-release testing into ongoing AI assurance.

Modern AI testing extends beyond running conversations through an agent. Teams also need confidence that the evaluations themselves are accurate, consistent, and repeatable.

In our next blog, we’ll explore why evaluator accuracy plays such an important role in building trustworthy AI testing.

Want to talk to the Provar team? Book some time today!