Show HN: Lunette – auditing agents for evals and environments (fulcrumresearch.ai)

Lunette, a newly launched platform, empowers developers to audit their AI agents and evaluate environments using advanced investigator agents. It addresses common issues in AI assessments, such as the widespread presence of ill-posed tasks that can lead agents to fail unpredictably. Traditional benchmarks, like SWE-bench Verified, have glaring flaws—some tasks are unsolvable or poorly defined, hampering the understanding of an agent's true capabilities. Lunette's investigators re-enter the same environment as the agent to run experimental inquiries, providing a deeper analysis of failures, and producing verifiable evidence that enhances the quality of evaluations. For the AI/ML community, Lunette is significant because it introduces an innovative debugging method ideal for the complex nature of AI systems. By leveraging environment access and thorough validation processes, it avoids the pitfalls of confabulation common in large language model analysis. In testing Lunette against traditional methods, its performance demonstrated a marked improvement, emphasizing the critical role of real-time environment interaction in accurate problem diagnosis. This approach not only refines the evaluation process but also paves the way for more reliable AI development and deployment, ensuring that agents can be trained and assessed in practical, meaningful contexts.