Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation
arXiv:2606.01725v1 Announce Type: cross Abstract: Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes. Despite its popularity, its system-level behavior remains poorly understood, particularly for complex datasets and agent architectures-owing to highly non-deterministic execution, prohibitive evaluation costs, and limited visibility into proprietary models. This paper presents GAIATrace, the first token-level trace dataset of two state-of-the-art agentic systems (MiroThinker and OWL) running GAIA, a benchmark composed of a heterogeneous mi
The proliferation of agentic AI systems necessitates robust methods for understanding their behavior, especially as they become more complex and deployed in critical applications.
This research provides a foundational methodology and dataset for analyzing the performance and reliability of agentic AI, which is crucial for their development, deployment, and regulatory oversight.
The introduction of GAIATrace enables a more transparent, data-driven approach to evaluating agentic AI, shifting their characterization from black-box observations to detailed, token-level understanding.
- · AI developers
- · Machine learning researchers
- · AI ethics and safety organizations
- · Proprietary AI labs resistant to transparency
- · Systems with uninterpretable agent behaviors
Improved understanding of agentic AI system behavior through detailed trace data.
Faster development and debugging of more reliable and robust autonomous AI agents.
Enhanced trust and broader adoption of agentic AI in sensitive or high-stakes environments due to increased transparency and verifiability.
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