Plurai is building the recursive self-improvement layer for AI agents. As agents take on real work across the enterprise, the ones that last will be those that learn from their own experience, growing more capable, more precise and more efficient with every run. Plurai unlocks that evolution for any company's agents, and lets every company own the intelligence behind them instead of renting it.
This is the 2nd launch from Plurai. View more
Plurai Simulation
Launching today
Find your agent’s worst bugs before your users do. One command in Claude Code, Codex or GitHub Copilot builds a digital twin of your agent’s world: synthetic personas and documents, plus mocked tools, APIs and databases, in sandboxes that never touch production.
Free Options
Launch Team / Built With
Render Sandboxes: a computer for every agentRun untrusted code safely. Idle sandboxes cost nothing
Promoted
What's on the roadmap after this launch?
@volk13 So much! Were also hosting a webinar on dec 9th - https://luma.com/plurai-agent-simulation-dec26
thanks for the love today everyone 🙏
imagine your agent hitting its worst bugs before a single real user ever talks to it. that's Plurai Simulation: we build your agent its own world (tools, data, users who give wrong info and push back) and send it straight to the scenarios that matter most. it comes back with what broke, why, and the fix. plug in your prod traces and that world gets hyper-real.
your agent is going to fail somewhere. better it happens here first: https://simulation.plurai.ai
@reut_v_plurai and were here for any questions!
Impressive work guys! Congrats on the launch.
@david_klassen Thank you ✨
@david_klassen Thank you!
Testing actual state changes instead of just reading transcripts is so needed, congrats on the launch!😉
@kostfast Thank you for such kind words
@tammy_wolfson2 I’m curious, how do you measure whether an agent has actually improved ?
Congrats on the launch, team! We run AI agents that handle day-to-day work for e-commerce sellers (listings, orders, supplier sync), and the pain you're tackling is very real for us: an agent that hits a marketplace reject on Monday will happily make the same mistake on Friday. Learning from its own runs is exactly the missing piece. Love the "own the intelligence" angle too. Curious how you keep a bad lesson from sticking: is there a review step before an improvement goes live?