Patronus AI

What does Patronus AI do?

Patronus AI runs an evaluation and simulation stack for AI products, turning prompts, traces, datasets, and agent workflows into measurable signals. Its platform centers on Patronus Evaluators, Patronus Experiments, Patronus Datasets, and Patronus Logs/Traces so teams can score outputs, compare runs, and spot failure patterns before deployment. The product also includes specialized models like Glider and Lynx, plus Percival for agentic traces and planning errors. The company frames its work around Phase I static datasets and Phase II long-horizon real-world agent problems. It cites 1M+ world data artifacts, 5k+ expert contributors, 10,000 Q&A pairs, and 573 tip-of-the-tongue Q&A pairs as part of its research base. Patronus says its evaluator stack can deliver 30, 40% model lift and includes 20+ failure modes in agent traces. Customers highlighted on the site include Nova AI, Emergence AI, Weaviate, Etsy, Gamma, Exa, Hospitable.com, and Algomo.

Why use Patronus AI?

• Its evaluation stack combines datasets, experiments, logs, traces, and comparisons in one workflow, reducing tool switching during model iteration.
• Percival focuses on agentic traces and 20+ failure modes, which helps teams diagnose planning and reasoning issues instead of only scoring final outputs.
• Glider is a 3B evaluator LLM, giving teams a dedicated scoring model for custom criteria without building their own judge from scratch.
• Lynx is positioned for hallucination detection, so teams can target one of the most common failure classes in LLM applications.
• Enterprise deployment options include on-prem or dedicated VPC, custom data retention, and SSO for stricter security requirements.

Who is Patronus AI for?

• ML engineers who need repeatable evals for LLM outputs and agent traces.
• AI product teams who want to compare model variants before shipping changes.
• Research teams who need datasets and benchmarks for long-horizon agent work.
• Platform engineers who need centralized logging, traces, and comparisons for AI systems.
• Applied AI teams who need guardrails for hallucinations, memory, and planning errors.

What are Patronus AI's key features?

What does Patronus AI integrate with?

• Hugging Face
• NVIDIA
• MongoDB

What are Patronus AI's use cases?

How does Patronus AI work?

1. Connect your first model, prompt set, or agent trace in Patronus Logs so the platform can capture outputs, metadata, and failures from the start.
2. Define what good looks like with Patronus Evaluators, then score responses for hallucinations, memory slips, planning errors, and other task-specific checks.
3. Run Patronus Experiments to compare model variants, prompt changes, or agent policies side by side before you ship anything to production.
4. Inspect Patronus Traces and Patronus Comparisons to see where behavior diverges, reproduce bad runs, and isolate the exact step that broke.
5. Build reusable Patronus Datasets for regression testing, then keep rerunning them as your system changes so quality stays measurable over time.

How much does Patronus AI cost?

Frequently asked questions