Janus
Janus (rebranded to Kairos) · Coding
Janus is an AI testing platform that simulates human users to evaluate AI agents, catch hallucinations and stress-test behavior before you ship.
About Janus
What Is Janus
Janus is an AI testing platform built around one idea: you can't trust an AI agent until you've put it in front of users, so Janus creates those users for you. Its human simulation spins up custom populations of simulated people, sets them loose on your agent, and reports back on how the agent behaves across hundreds of conversations instead of a handful of manual tests. That AI user simulation stands in for the crowd of real customers you can't run tests with.
The problem it targets is real. Teams ship agents that answer support tickets, book appointments or walk customers through a purchase, then find out at scale that the agent invents policy, drops context or fails on an edge case nobody scripted. Manual QA can't cover that surface, and a single reviewer brings a single bias. Janus replaces spot checks with a simulated crowd that runs the same scenarios over and over.
Two limits are worth knowing up front. A simulated user is still a model, so it reflects whatever blind spots that model carries, which means simulation sharpens coverage but doesn't replace live feedback from real customers. The product is also aimed at teams building or running agents rather than at casual end users, and pricing and availability have shifted as the company rebranded its site from Janus to Kairos.
Getting Started
- Connect the AI agent or endpoint you want to test, and describe what it's meant to do
- Define a population of simulated users, setting traits, goals and the kinds of behavior you want to probe
- Run the population against the agent and let it work through the scenarios you set up
- Review the results for failures, hallucinations and inconsistencies, then fix the agent and run again
Product Information
A quick look at Janus's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- AI product teams shipping agents
- QA and test engineers
- Startup founders validating a new agent
- Support and operations leads
Tasks
- Hallucination checks
- Edge-case coverage
- Regression testing after a change
- Consistency testing
- Pre-launch stress testing
Scenarios
- An agent launches next week and you have no idea how it handles a frustrated customer
- You swapped the underlying model and want to know what regressed
- A support bot keeps promising refunds it can't issue
- You're pitching an agent to a client and need evidence it holds up
Key features
Simulated User Populations
Janus builds custom populations of AI users that stand in for real customers. You define who these simulated people are, what they want and how they behave, then point the whole group at your agent. That lets you cover hundreds of conversations and persona types without recruiting a single live tester, which is the core of how the platform finds problems at scale.
Hallucination Detection
The platform watches for answers that go beyond what the agent should say, whether that's an invented refund policy, a made-up price or a fact the agent has no basis for. Catching those moments in a simulated run matters because hallucinations are exactly the failures that slip past scripted tests and then surface in front of a real customer.
End-to-End Agent Testing
Janus frames testing as more than a pass or fail. It runs agents through a range of scenarios and reports on behavior across the run, so you see where the agent stays consistent and where it drifts. For teams that treat evaluation as an ongoing job rather than a one-time gate, that steady signal is the main value.
Custom Scenario Setup
You shape the test to match your product. Set the goals simulated users pursue, the types of conversations they push toward and the edge cases you care about, so the run reflects your agent's real job instead of a generic benchmark. This is what makes the output useful for a specific agent rather than a generic score.
Results You Can Act On
Runs come back as findings a team can work from: which conversations failed, what the agent said wrong and where behavior varied. That turns a vague worry about an agent into a list of concrete failures to fix, and it's the loop the platform is built around.
Pros and cons
Pros
- Tests an agent against hundreds of simulated conversations in one run, far beyond what manual spot checks reach
- Hallucination detection targets the failures that scripted tests miss and real users notice
- Custom populations let you match the simulated users to your actual customer base instead of a generic crowd
- Repeatable runs make it possible to compare an agent before and after a change, so regressions are visible
- Fits teams that already run an evaluation pipeline and want to push coverage wider
Cons
- Simulated users are models too, so the results carry their blind spots and don't stand in for live customer feedback
- It's built for teams building or running agents, so there's no value for someone who just wants to chat with an AI
- Pricing and public availability aren't settled while the company rebrands from Janus to Kairos, which makes budgeting harder to pin down
- The output depends on how well you set up your scenarios, so a weak test plan produces weak findings
Frequently asked questions
Janus tests AI agents by simulating human users. You define a population of AI users, point them at your agent and review how it behaves across many conversations, including where it hallucinates or fails on edge cases.
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