The simulation harness that improves your agent every run.
Plurai builds a digital twin of your agent’s environment, simulates the full space of interactions it will ever face, surfaces the failures you’d never test for, and fixes them.
Your environment, fully rebuilt with zero integration.
Not a shallow user-agent chat. Plurai reads your agent as built, decides what stays real and what gets mocked, and rebuilds the world it runs in, so the whole flow is under control, end to end, with no integration work on your side.
Scenarios pulled from reality, not invented.
We build tests from your actual data: traces, knowledge bases, PRDs, whatever you have. We carry the real statistics, anomalies and noise into them, so your agent is tested against what production actually looks like, not a clean-room version of it.
Coverage that’s engineered, not guessed.
We model the full space of what your agent can do, probabilistically, with real data science, then generate the paths to cover it. Not a hand-picked set that hopes it caught the important cases.
The hardest cases are the least likely ones. That is exactly why you never wrote them.
From weeks of testing to a single run.
A simulated user plays every scenario against your agent, over the mocked world. Every eval is scored, and every score carries the reasoning behind it, in your repo and in the observability platform you already use.
Every failure, traced to the line that caused it.
Plurai root-causes each failure, separates real agent bugs from suite noise, and lands the fix with the evidence attached: ranked, and pointing at the exact code location. Then runs again to prove it held.
Your agent can take the right action and still fail. Reading the output would never have told you.
Every run closes the loop.
Not just report failures. Act on them, and run again to confirm.
The simulation harness that improves your agent every run.
Plurai builds a digital twin of your agent’s environment, simulates the full space of interactions it will ever face, surfaces the failures you’d never test for, and fixes them.
Your agent, as built.
Prompts, tools, sub-agents, schemas, policy docs. Plurai reads it as it is. Nothing to instrument, nothing to annotate.
Your environment, fully rebuilt with zero integration.
Plurai decides what stays real and what gets mocked, then rebuilds the world your agent runs in, so the whole flow is under control, end to end.
Scenarios pulled from reality, not invented.
Optional. We build tests from your actual data: traces, knowledge bases, PRDs, whatever you have. The real statistics, anomalies and noise carry into them.
Coverage that’s engineered, not guessed.
The full space of what your agent can do, modelled probabilistically, then the paths to cover it, generated. Complexity is a dial, not a guess.
From weeks of testing to a single run.
A simulated user plays every scenario against your agent, over the mocked world. Every eval is scored, and every score carries its reasoning.
Every failure, traced to the line that caused it.
Root-caused, ranked, and pointing at the exact code location, with the evidence attached. Your agent can take the right action and still fail.
Every run closes the loop.
Not just report failures. Act on them, and run again to confirm the fix held.
Shipping reliable agents is hard.
Your agent has endless ways to fail.
Interaction paths, tools, world state: the combinations are endless, and shallow testing barely scratches the complexity your agent meets in production.
Testing is slow and inefficient.
Building the agent was the fast part. Now you’re hand-tagging data and crafting small test sets, work that eats weeks and still barely moves your coverage.
Even when you find an issue, fixing it is a gamble.
Patch one failure and two more surface where you weren’t looking. Nothing tells you the fix actually held, or just pushed the problem somewhere you’re not watching.
How Plurai is different.
Scenarios pulled from reality, not invented.
We build tests from your actual data: traces, knowledge bases, PRDs, whatever you have. We carry the real statistics, anomalies and noise into them, so your agent is tested against what production actually looks like, not a clean-room version of it. Optional: the loop runs without it.
Fixes that hold, because we see the whole picture.
Only a system running inside your stack, against your environment and full coverage, can find the actual failure and prove the fix. Every finding carries its root cause, its evidence, and the file and lines it lives at. Not shallow references. Not guesswork. The problem, solved and verified.
Every run closes the loop.
Coverage that’s engineered, not guessed. Your stack, your environment, the whole picture. No shallow references. No guesswork. The problem, solved and verified.