Evaluation methods reveal risks in enterprise AI analytics agents
Comparative Analysis of Agent Evaluation Frameworks: Stability, Detection, and Discovery in Enterprise Analytics Agents
Artificial IntelligenceComputation and LanguageMachine LearningSoftware Engineering
Summary
Evaluating AI agents that analyze enterprise data is tricky because they use many tools and can fail in subtle ways. The authors compare three ways to check these agents' work, finding some methods catch more real problems while others produce many false alarms. They also find that testing for security issues and testing for normal function reveal different problems, so both are needed. Their study shows certain evaluation methods find errors humans might miss.
What this means in practice
- •For enterprise analytics teams: Improve monitoring of AI analytics agents by combining trace-based and red-team evaluations to detect both functional and security failures.
- •For security operations teams: Use adversarial testing methods alongside functional checks to find subtle security risks in AI-powered analytics workflows.
Authors
Howard Zhang, Chuck Arvin, Logan Brown, Mihir Bhatia, Neelu Choudhary, Lily Miao
Abstract
Deploying LLM-based analytics agents in enterprise settings requires evaluation frameworks that can reliably detect failures across complex, multi-tool workflows. We present a three-phase comparative study of three evaluation frameworks, each representing a distinct evaluation paradigm (trace-based LLM judging, text-only LLM judging with red-teaming, and deterministic heuristics), applied to two analytics agents in a controlled research setting using frozen execution traces. Phase 1 quantifies evaluation harness stability through repeated measurements, finding that 97% of configurations meet the stability threshold and that stability patterns are agent-independent. Phase 2 measures known risk detection on 72 ground-truth test cases, where the trace-based framework achieves the best precision-recall balance (F1=0.90) while text-only evaluation matches on recall but generates 5x more false alarms. Phase 3 evaluates unknown risk discovery, uncovering 24 novel failures with zero overlap between functional testing and security red-teaming. Our results demonstrate that (1) the information available to the evaluation harness determines evaluation quality, (2) functional and security evaluation are complementary, and (3) LLM-based evaluation harnesses surface subtle errors that human reviewers miss. We recommend a two-framework strategy: trace-based evaluation for functional assessment and adversarial red-teaming for security validation.