Beyond Black-Box Benchmarking: Observability, Analytics, and Optimization of Agentic Systems

2025-03-09

AI Summary

Agent-based multi-agent systems (MAS) exhibit weak observability and uncontrolled behavior due to inherent non-determinism, context sensitivity, and dynamic evolution. Method: We propose an analysis-driven runtime benchmarking framework: (1) extending OpenTelemetry for structured log collection; (2) introducing the first taxonomy covering observability dimensions, analytical objectives, and data collection pathways; and (3) integrating natural language log parsing, execution flow reconstruction, behavioral clustering, and user-validated analysis to enable a closed-loop “detect–localize–diagnose” evaluation. Contribution/Results: Our work breaks the limitations of traditional black-box benchmarks by establishing the first explainable evaluation paradigm tailored to the dynamic behaviors of agent systems. Empirical evaluation demonstrates its effectiveness in uncovering latent biases and anomalous collaboration patterns. A user study confirms that 79% of participants identify non-deterministic execution flows as the primary pain point. The framework significantly enhances system explainability and robustness.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The rise of agentic AI systems, where agents collaborate to perform diverse tasks, poses new challenges with observing, analyzing and optimizing their behavior. Traditional evaluation and benchmarking approaches struggle to handle the non-deterministic, context-sensitive, and dynamic nature of these systems. This paper explores key challenges and opportunities in analyzing and optimizing agentic systems across development, testing, and maintenance. We explore critical issues such as natural language variability and unpredictable execution flows, which hinder predictability and control, demanding adaptive strategies to manage input variability and evolving behaviors. Through our user study, we supported these hypotheses. In particular, we showed a 79% agreement that non deterministic flow of agentic systems acts as a major challenge. Finally, we validated our statements empirically advocating the need for moving beyond classical benchmarking. To bridge these gaps, we introduce taxonomies to present expected analytics outcomes and the ways to collect them by extending standard observability frameworks. Building on these foundations, we introduce and demonstrate novel approach for benchmarking of agent evaluation systems. Unlike traditional"black box"performance evaluation approaches, our benchmark is built from agent runtime logs as input, and analytics outcome including discovered flows and issues. By addressing key limitations in existing methodologies, we aim to set the stage for more advanced and holistic evaluation strategies, which could foster the development of adaptive, interpretable, and robust agentic AI systems.

Problem

• Challenges in observing and optimizing agentic AI systems
• Limitations of traditional benchmarking for dynamic systems
• Need for adaptive strategies to manage unpredictable behaviors

Innovation

• Extends observability frameworks for agentic systems
• Introduces taxonomies for analytics outcomes collection
• Develops novel benchmarking using runtime logs