Powerful 
AI Evaluation

The best way to ship top-tier AI products. Based on industry-leading AI research and tools

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Explore the Patronus Product

Our Core Eval Platform

Evaluators, Experiments, Logs Comparisons, Datasets, Traces

Percival

An Eval Copilot for Agentic Systems. Percival helps you analyze complex traces and identify 20+ agentic failure modes

RL Environments

Dynamic, feedback-driven environments for domain-specific agent training and evaluation

Discover our Areas of Experience

We have experience evaluating support responses to prevent hallucinations, maintain tone, develop guardrails, and understand assumptions

Nova AI
Using Patronus AI's Percival to

Auto-Optimize AI Agents for Code Generation

Case Study

“Automated prompt fixes are awesome — it plugs straight into our revision cycle. Eventually, I’ll probably just feed the suggested prompt edits into a ‘revise my prompt’ prompt. It’s like infinite prompt recursion, and I kind of love it”

— Paul Modderman, Founding Engineer

60x productivity boost by reducing agent debugging time from 1h to 1min
Automated prompt suggestions fixed 3 agent failures in 1 week
Increased agent accuracy by 60%
on SAP tool dataset through experimentation
Emergence AI
Partnering with Patronus AI to

Enhance and Govern Self-Generating Agents at Scale

Case Study

“Emergence’s recent breakthrough—agents creating agents—marks a pivotal moment not only in the evolution of adaptive, self-generating systems (...) which is precisely why we are collaborating with Patronus AI.”


—  Satya Nitta, Co-founder and CEO

Percival helped identify points of failure with human-in-the-loop
Percival encouraged analysis of existing tools before creating new ones
Percival requested human verification after tool creation and before usage
Weaviate
Leveraging Patronus AI's Percival to

Accelerate Complex AI Agent Development

Case Study

"It is an extremely helpful and interesting tool for building Agent systems. I think the best compliment that I can give as a developer is that this really made me feel empowered to test out a new idea."

— Developer at Weaviate

Reduced debugging time with the "Generate Insights" feature in Percival
Actionable, automated fixes with Percival’s "suggested prompt fix" capability
Percival caught subtle errors in tools, prompts, and logic, making the AI system more dependable
Etsy
Using Patronus AI’s Multimodal Judge to

Improve and Scale Image Captioning

Case Study

One key use case the Etsy AI team is leveraging generative AI for is autogenerating captions on product images to speed up listing. However, they kept running into quality issues — the captions often contained errors and unexpected outputs.

Evaluated and improved automated image caption quality at scale
System looked at caption accuracy, relevance, and hallucination
Data-driven iteration to boost captioning reliability
Gamma
Scaling AI Performance with

Automated Evals and Rigorous Experimentation

Case Study

"Patronus helped us uncover error patterns and optimize AI outputs (...)
It became an invaluable part of our evaluation workflow."

— Jon Noronha, Co-Founder

1K+ hours saved on manual evaluation per month through Patronus Judges
15+ LLMs benchmarked with Patronus Experiments
10K+ real-world samples distilled into one coherent ground truth dataset
Nova AI
Nowa AI
Using Patronus AI's Percival to

Auto-Optimize AI Agents for Code Generation

“Automated prompt fixes are awesome — it plugs straight into our revision cycle. Eventually, I’ll probably just feed the suggested prompt edits into a ‘revise my prompt’ prompt. It’s like infinite prompt recursion, and I kind of love it”

— Paul Modderman, Founding Engineer

Case Study
Emergence AI
Partnering with Patronus AI to

Enhance and Govern Self-Generating Agents at Scale

“Emergence’s recent breakthrough—agents creating agents—marks a pivotal moment not only in the evolution of adaptive, self-generating systems (...) which is precisely why we are collaborating with Patronus AI.”


—  Satya Nitta, Co-founder and CEO

Case Study
Weaviate
Weaviate
Leveraging Patronus AI's Percival to

Accelerate Complex AI Agent Development

"It is an extremely helpful and interesting tool for building Agent systems. I think the best compliment that I can give as a developer is that this really made me feel empowered to test out a new idea."

— Developer at Weaviate

Case Study
Etsy
Using Patronus AI’s Multimodal Judge to

Improve and Scale Image Captioning

One key use case the Etsy AI team is leveraging generative AI for is autogenerating captions on product images to speed up listing. However, they kept running into quality issues — the captions often contained errors and unexpected outputs.

Case Study
Gamma
Scaling AI Performance with

Automated Evals and Rigorous Experimentation

"Patronus helped us uncover error patterns and optimize AI outputs (...)
It became an invaluable part of our evaluation workflow."

— Jon Noronha, Co-Founder

Case Study

How Patronus Works

A unified workflow to evaluate, debug, and improve AI agents

Evaluate

Use Patronus evaluators for top use cases like hallucination, multimodal, or bring-your-own

→  Use 50+ turnkey evaluators for domain-specific use or bring your own
→  Create LLM-as-judge, via SDK, via API, and select any backing model

Benchmark

Understand improvement areas across the provided or custom evaluation criteria

→ Benchmark LLMs, RAG systems, and agents side-by-side with Comparisons
→  Compare model output and view evaluator scoring and reasoning

Improve

Create and apply custom datasets or prompts in LLM testing

→ Use off-the-shelf datasets for areas like security, hallucination, finance, etc.
→ Construct datasets on the platform with evaluation results or human labels

Evaluate

Use Patronus evaluators for top use cases like hallucination, multimodal, or bring-your-own

→  Use 50+ turnkey evaluators for domain-specific use or bring your own
→  Create LLM-as-judge, via SDK, via API, and select any backing model

Benchmark

Understand improvement areas across the provided or custom evaluation criteria

→ SBenchmark LLMs, RAG systems, and agents side-by-side with Comparisons
→  Compare model output and view evaluator scoring and reasoning

Improve

Create and apply custom datasets or prompts in LLM testing

→ Use off-the-shelf datasets for areas like security, hallucination, finance, etc.
→ Construct datasets on the platform with evaluation results or human labels

Chat

Simplify agent eval with a chat-based copilot

→ Identify recurring issues and patterns
→  Explore trace components with detailed span analysis

Analyze

Analyze and debug complex traces

→ Tracing and Logging for every step of RAG, agentic, and other frameworks
→ Eval across a broad error taxonomy and add your own

Generate

Generate insights and fixes automatically

→ Optimize prompts with suggested fixes
→ AIterate on solutions with point of failure insights

Chat

Simplify agent eval with a chat-based copilot

→ Identify recurring issues and patterns
→  Explore trace components with detailed span analysis

Analyze

Analyze and debug complex traces

→ Tracing and Logging for every step of RAG, agentic, and other frameworks
→ Eval across a broad error taxonomy and add your own

Generate

Generate insights and fixes automatically

→ Optimize prompts with suggested fixes
→ AIterate on solutions with point of failure insights

Train

Train agent in a dynamic, feedback-driven setting

→ Utilize verifiers, task-oriented rewards, and interruption success measures
→  Allows measurement of duration, turns, and other iterations to achieve the outcome

Specialize

Have realistic, domain-specific applications

→ Capture real-world domain rules and boundaries to establish space
→  Test in ecologically valid settings with human-centric interruptions

Customize

Configure difficulty level for environment and tasks

→ Have varying levels of ambiguity with tasks, probing exploration
→ Distract with more interruptions or distractions

Train

Train agent in a dynamic, feedback-driven setting

→ Utilize verifiers, task-oriented rewards, and interruption success measures
→  Allows measurement of duration, turns, and other iterations to achieve the outcome

Specialize

Have realistic, domain-specific applications

→ Capture real-world domain rules and boundaries to establish space
→  Test in ecologically valid settings with human-centric interruptions

Customize

Configure difficulty level for environment and tasks

→ Have varying levels of ambiguity with tasks, probing exploration
→ Distract with more interruptions or distractions
Explore Customer Flows

Research-first benefits

Lynx

SOTA hallucination detection model
Lynx is the first model that beats GPT-4 on hallucination tasks
Lynx (70B) achieved the highest accuracy at detecting hallucinations

FinanceBench

Industry-first benchmark for LLM performance on financial questions
High-quality, large-scale set of 10,000 Q&A pairs
Based on publicly available financial documents

BLUR

Evaluate agent effectiveness in tip-of-the-tongue moments
Identify something a person can vaguely remember not name
Curated, high-quality dataset with 573 tip-of-the-tongue Q&A pairs

GLIDER

Evaluation model that produces high-quality reasoning chains and highlights 
Can help make its decisions more explainable
Cost-effective for companies requiring efficient, fast, and reliable guardrails

Our latest update

September 25, 2025

Percival Chat: An Eval Copilot for Agentic Systems

Chat, your eval co-pilot, aims to simplify the agent evaluation process even more by providing you with in-context guidance on tracing, integrations, evaluation criteria, and prompting.

October 14, 2025

Introducing MEMTRACK: A Benchmark for Agent Memory

August 14, 2025

Prompt Tester: Faster Iterations on Your Prompts

August 21, 2025

Meet the Patronus team: Josh Weimer

July 31, 2025

Prompt Management: An Easier Way to Organize and Optimize Your Prompts

September 25, 2025

Introducing MEMTRACK: A Benchmark for Agent Memory

September 25, 2025

Prompt Tester: Faster Iterations on Your Prompts

September 25, 2025

Meet the Patronus team: Josh Weimer

September 25, 2025

Prompt Management: An Easier Way to Organize and Optimize Your Prompts