AI Agent Deployment Platform with Automated Evals and Guardrails for Teams
Get CI/CD-style safety for your AI agents — run automated evals before deployment, catch failures in staging, and deploy with confidence.
The signal
“Every team I've talked to deploys agents differently. Some bolt it onto the CI/CD and some run evals manually before pushing. Some just ship directly and then see what's breaking in prod. Here, the agent itself isn't the confusing part anymore. The actual tricky part is knowing w”r/aiagents — read the original
Why it scores 78
Teams deploying AI agents face real deployment reliability problems but many still rely on makeshift internal solutions rather than seeking specialized tools.
No dominant solution exists yet, with only fragmented open-source projects and platform-specific tools rather than unified deployment frameworks.
A solo dev could build a monitoring/rollback MVP using existing observability APIs, but agent deployment infrastructure is complex.
Agent adoption is accelerating while deployment practices remain immature, creating immediate need for production-grade solutions.
MVP build path
A FastAPI backend that ingests agent code via a GitHub App, runs a predefined set of pytest-based evals in a sandboxed Docker container, and reports pass/fail status on pull requests.