Automated evaluation and regression testing for AI agents, integrated with GitHub.
Product memo
AI developers use Regent for automated evaluation and regression testing of AI agents. It integrates directly into GitHub pull requests, commenting on code changes and flagging regressions. This approach removes the need for manual test case writing, helping teams improve the reliability of AI applications.
For who
AI developers and teams
Solves what
Automated evaluation and regression testing for AI agents.
- Self-writing evals
- Tests on every PR
- GitHub regression comments
In their own words
Evals that write themselves. Tests that run on every PR.
Regent generates evals from your production traffic and flags regressions as GitHub comments — no test cases to write, no scoring functions to define.
Automated Evals for AI Agents
Commercial cues
Model
free only
Free tier
Yes
Trial
No
Pricing Strategy
Regent offers a free-only tier, making its automated AI agent evaluation tools accessible to all developers.
- • A free tier encourages broad adoption and integration into developer workflows.
- • Automated PR comments reduce friction by living inside the developer's daily work.
- • No credit card required lowers the barrier to entry for new users.
Operator context
Founded
Apr 2026
Platform
Web app
Audience
Developers
Public footprint
Builder Strategy
- Strategy Type
- Niche Specialist
- Stage
- Pre Revenue
- Effort
- Solo Buildable
About Regent Expand
Regent provides automated evaluation and regression testing tailored for AI agents. It targets AI developers and teams by integrating directly into their existing GitHub workflows.
The platform generates evaluations from production traffic and flags regressions within pull requests, supporting models from OpenAI and Anthropic. This helps teams maintain the reliability of their AI applications without manual test case creation or scoring function definitions, making it a practical tool for improving AI agent quality.