Reddi Agent Evaluationv1.0.2

Evaluate an AI customer service agent for handling diverse customer inquiries, ensuring consistent response quality and adherence to company policies. Use behavioral contract testing to define expected interaction patterns and statistical evaluation to measure reliability across multiple test runs.

Assess an AI financial advisor agent's capability to provide accurate investment recommendations and regulatory compliance. Implement adversarial testing to simulate edge cases like market crashes and multi-dimensional evaluation to prevent gaming of performance metrics.

Test an AI diagnostic agent for reliability in interpreting medical data and suggesting treatments, focusing on flaky test handling due to variable inputs. Use capability assessment to ensure it meets clinical standards and regression testing to catch behavioral drifts after updates.

Benchmark an autonomous driving agent's decision-making in simulated traffic scenarios, emphasizing reliability metrics and behavioral invariants. Apply statistical test evaluation to analyze performance distributions and prevent data leakage from test environments into training.

Offer a cloud-based service where companies can upload their AI agents for automated evaluation, including behavioral regression tests and reliability scoring. Revenue is generated through subscription tiers based on test volume and advanced features like adversarial testing modules.

Provide expert consulting services to help organizations design and implement custom evaluation frameworks for their LLM agents, focusing on bridging benchmark and production gaps. Revenue comes from project-based fees and ongoing support contracts for monitoring and optimization.

Distribute the evaluation skill as open-source software to foster adoption, while offering paid enterprise support, customization, and integration services. Revenue is derived from support contracts, training workshops, and premium features for large-scale deployments.