CekuraAutomated testing and monitoring for AI agents to ensure reliable production performance.

4.2 (5)

Reviewed by Daniel Nikulshyn·Updated May 2026

AI Agents Testing Monitoring QA Analytics DevTools Conversational AI

Overview

Cekura is a quality assurance platform built for AI agents, helping teams validate that their conversational and autonomous systems behave as expected before and after deployment. It runs simulated interactions, evaluates responses against defined criteria, and surfaces regressions early in the development cycle. Beyond pre-launch testing, Cekura provides ongoing monitoring of live agents, tracking performance, accuracy, and edge-case failures over time. This gives engineering and product teams visibility into how their AI behaves in real-world conditions and where it needs improvement. The platform is aimed at developers and businesses deploying voice or chat-based AI agents who need confidence that their systems remain consistent, safe, and effective across updates.

Key features

Simulated agent conversation testing
Performance and accuracy evaluation
Live production monitoring
Regression detection across versions
Edge-case and failure analysis
Reporting and analytics dashboards

Pricing

Model: Freemium
Category: Information Agents
Rating: 4.2 / 5 (5)

Use cases

Pre-Launch Validation of Conversational Agents

Run simulated interactions against chat or voice agents to verify expected behavior and catch issues before deploying to production.

Regression Detection Across Agent Versions

Automatically compare agent performance between versions to identify regressions introduced by prompt changes, model updates, or new logic.

Live Production Monitoring

Continuously track accuracy and performance of deployed AI agents in real-world conditions, surfacing failures and drift over time.

Edge-Case and Failure Analysis

Identify rare or problematic scenarios where agents underperform, giving teams targeted insights for improvement and retraining.

Pros & Cons

Pros

Automated testing reduces manual QA effort
Catches regressions before production deployment
Continuous monitoring of live agent behavior
Helps surface edge cases and failure modes

Cons

Requires setup and test case definition
May not cover every domain-specific scenario
Best value for teams with mature AI deployments

Battle record

Across 1 battle in the Pantheon.

1st

2nd

3rd

Last battle

#8
Information Agents Showdown — February 15, 2024
Feb 15, 2024 · #8 of 9

Reviews

4.2

Average from 5 ratings.

Jamal Carter

May 10, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is performance and accuracy evaluation — handled better than most — and catches regressions before production deployment. Requires setup and test case definition is my one real gripe. Worth the time if this is your use case.

Wei Chen

Mar 14, 2026

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on reporting and analytics dashboards, and continuous monitoring of live agent behavior caught me off guard. still, I'd recommend giving it a real trial.

Marcus Bell

Feb 24, 2026

Use it every day

Honestly didn't expect to like it this much. Performance and accuracy evaluation is exactly what I needed, and catches regressions before production deployment. I do wish requires setup and test case definition, but I reach for it almost every day now and it just clicks.

Beatriz Costa

Dec 30, 2025

Solid for our team

We rolled this out across the team last quarter and continuous monitoring of live agent behavior. Performance and accuracy evaluation fits neatly into how we already work, and performance and accuracy evaluation removed a step we used to do by hand. Requires setup and test case definition, which is the main caveat, but it has held up under daily use.

Tomáš Novák

Sep 1, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on regression detection across versions, and continuous monitoring of live agent behavior caught me off guard. May not cover every domain-specific scenario is why this isn't a perfect score, still, I'd recommend giving it a real trial.