dystopic.ai provides an agent evaluation platform that models the production environments where AI agents operate spaces that hold state and respond to reads and writes so teams can observe behavior before deployment. It offers a scenario suite covering common workflows, edge cases, and failure modes, along with grading that checks both the agent's actions and the resulting world state against declared constraints andrelevant business context. The platform also tracks variance across repeated runs to distinguish genuine regressions from inherent non-determinism.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Funding not disclosed
Founders
Product
Problem
AI agents are non-deterministic by definition, so developers cannot rely on unit tests or exact-output matching to verify behavior. When agents gain read and write access to production systems, incorrect actions create real-world consequences, yet current evaluation methods rely on production tracing which only reveals failure after it has already impacted users.
Solution
dystopic.ai builds production-symmetric environments that model the systems an agent will read from and write to, allowing teams to observe behavior without causing production consequences. The platform provides a suite of representative scenarios that cover common workflows, edge cases, high-consequence actions, and known failure modes. Each run is graded on both the agent's trajectory and the state the simulated world was left in, against constraints the team declares plus failure patterns drawn from other domains. The system also accumulates behavioral history across versions, enabling teams to distinguish genuine regressions from ordinary variance in non-deterministic systems.
Target Audience
Primary customers are engineering and AI teams at companies deploying agents with access to internal systems, APIs, databases, and downstream actions who need to establish confidence in behavior before production release.
Features
- Stateful world model where reads reflect writes the agent made, verified through checks like refund-retry-reads-back that confirm calls resolve against the same simulated world
- Variance analysis that runs identical agents multiple times to detect and retire unstable scenarios before any threshold is set
- Trajectory logging that records actual tool calls, their returns, and resulting state changes, enabling detection of repeat calls, loops, and dead ends without scoring against a pre-written route
- Grading methodology combining explicit invariants with business-specific judges for subjective dimensions like task resolution and grounding in context
- Failure-to-scenario conversion pipeline that turns discovered production failures into ongoing screens for future deployments
- Isolation between runs so hundreds of scenarios execute independently against the simulated world