Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability

TL;DR

This paper examines the limitations of existing interpretability methods for agentic AI systems, emphasizing the need for new techniques to ensure safety, accountability, and oversight across their complex, goal-directed behaviors.

Contribution

It identifies gaps in current interpretability approaches for agentic systems and proposes future research directions for developing system-level oversight tools.

Findings

Current interpretability methods are insufficient for agentic systems.

Agentic systems require new, tailored interpretability techniques.

Embedding oversight mechanisms is crucial for safe deployment.

Abstract

Agentic systems have transformed how Large Language Models (LLMs) can be leveraged to create autonomous systems with goal-directed behaviors, consisting of multi-step planning and the ability to interact with different environments. These systems differ fundamentally from traditional machine learning models, both in architecture and deployment, introducing unique AI safety challenges, including goal misalignment, compounding decision errors, and coordination risks among interacting agents, that necessitate embedding interpretability and explainability by design to ensure traceability and accountability across their autonomous behaviors. Current interpretability techniques, developed primarily for static models, show limitations when applied to agentic systems. The temporal dynamics, compounding decisions, and context-dependent behaviors of agentic systems demand new analytical approaches. This paper assesses the suitability and limitations of existing interpretability methods in the context of agentic systems, identifying gaps in their capacity to provide meaningful insight into agent decision-making. We propose future directions for developing interpretability techniques specifically designed for agentic systems, pinpointing where interpretability is required to embed oversight mechanisms across the agent lifecycle from goal formation, through environmental interaction, to outcome evaluation. These advances are essential to ensure the safe and accountable deployment of agentic AI systems.