G2

George U.

Co-Founder

Small-Business (50 or fewer emp.)

9/22/2026

"Our Experience Testing AI Agents with Netra"

5/5

What do you like best about Netra?

What I like most about Netra is that it helps me understand why an AI agent failed. I can follow the full trace to see the model calls and tool decisions behind an answer, instead of guessing from the final response. The interface makes those results easy to follow, and having text, voice, and image evaluations in one place is useful when working across different AI applications.

The integrations are another strength. The SDKs make it possible to bring evaluations into a development workflow, run tests after changes, and go straight from a failed test to its trace. I also like seeing pass rates alongside latency, duration, and cost. An agent may give the right answer, but response time matters just as much in a voice interaction.

Onboarding was straightforward, and the support was helpful when working out how to set up meaningful evaluations. The free plan gives teams room to try the platform, while the paid plans scale with usage. I would still keep an eye on span volume as testing grows. For me, the value is having the evidence to investigate failures and track performance in one place, with less time spent pulling results together from separate tools. Review collected by and hosted on G2.com.

What do you dislike about Netra?

You really need a vendor demo to understand the product’s full capabilities. There are not many public forums where you can find discussions around the platform. Review collected by and hosted on G2.com.

What problems is Netra solving and how is that benefiting you?

Netra solves a problem we were running into with AI agent testing. You often end up using different tools for functional testing, security, and testing voice, text, or image interactions. Netra brings all of this into one place.

What I find useful is that it goes beyond checking whether an agent gave the right answer. It helps uncover reasoning failures, hallucinations, prompt injection risks, unsafe responses, and issues with multimodal interactions. When something fails, we also get better visibility into where things went wrong.

That saves time during testing and makes debugging easier. More importantly, it helps uncover problems that could easily be missed when different teams are testing different parts of the agent separately. It gives us much more confidence before moving an AI agent into production. Review collected by and hosted on G2.com.