Unlock confidence in deployment with a multi-layered evaluation system that ensures agents meet business, technical, and ethical standards
Test agents across scenarios, workflows, and environments to verify task completion and correctness
Evaluate agents against organizational policies, ethical AI guidelines, and evolving regulatory frameworks
Measure latency, cost, and resource usage to ensure optimal performance at enterprise scale
Deliver predictable, verifiable outputs that stakeholders can trust, supported by audit trails and reporting
Unlock a structured way to test, measure, and refine AI agents with an easy-to-use, modular evaluation framework
Query and test AI agent performance without needing technical expertise or complex configurations
Benchmark prompts across multiple models to identify accuracy, reliability, and cost-effectiveness
Design flexible evaluation flows that adapt to different use cases, workflows, and enterprise systems
Generate actionable scoring, continuous feedback, and optimization pathways to refine agent performance
Use Cases
Evaluate autonomous AI agents with precision to ensure accuracy, safety, and reliability. Deliver actionable insights that improve performance, reduce risks, and build trust for real-world deployment
For Enterprises
Guarantee safe and compliant deployment of autonomous agents in mission-critical workflows like finance, operations, and IT
For SaaS Providers
Test and refine product-integrated agents for consistent customer experience and reduced error rates
For E-commerce
Ensure AI-powered personalization, recommendation, and pricing agents perform reliably under dynamic demand patterns
For Regulated Industries
Evaluate agents for alignment with frameworks like GDPR, HIPAA, and the EU AI Act before production rollout
For Governments & Public Sector
Assess and validate AI agents for transparency, fairness, and security to support public services, defense, and citizen trust
Agent evaluation revolutionizes AI performance management by providing structured, comprehensive assessment frameworks for smarter, more reliable autonomous operations
Empower development and operations teams to establish clear evaluation objectives—no guesswork required
Define precisely: Set specific evaluation goals, metrics, and expected outcomes for real-world AI agent performance
Ask strategically: Use structured frameworks to determine purpose, outcomes, and practical application scenarios intuitively
Integrate testing datasets from all sources for a centralized, accurate view of agent performance evaluation
Unify datasets: Aggregate representative data across diverse inputs, real-world scenarios, and testing environments
Enable understanding: Get complete visibility by synchronizing all evaluation inputs into one intelligent assessment framework
Monitor agent performance live and respond instantly to failures or anomalies with intelligent evaluation protocols
Trigger assessments: Get detailed analysis of individual workflow steps, API calls, and decision-making processes
Accelerate optimization: Make faster, performance-conscious improvements with contextual evaluation awareness
Harness adaptive LLM-as-a-judge systems to predict agent reliability and guide strategic development planning
Adapt models: Continuously refine performance projections using rule-based approaches and semantic evaluation methods
Forecast improvements: Simulate optimization scenarios and predict outcomes for more informed agent development
Empower development and operations teams to establish clear evaluation objectives—no guesswork required
Integrate testing datasets from all sources for a centralized, accurate view of agent performance evaluation
Monitor agent performance live and respond instantly to failures or anomalies with intelligent evaluation protocols
Harness adaptive LLM-as-a-judge systems to predict agent reliability and guide strategic development planning