Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation

· Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

GAIATrace and Vidur-Agent are introduced as novel tools for characterizing multi-model agentic AI systems on general tasks. GAIATrace is the first token-level trace dataset, capturing the full reasoning tokens, task-level structures, and activities of major participating LLMs from two state-of-the-art agentic systems, MiroThinker and OWL, as they run the GAIA benchmark. This benchmark comprises a heterogeneous mix of general-purpose tasks. Unlike previous datasets, GAIATrace provides deep visibility into highly non-deterministic agentic execution, addressing challenges like prohibitive evaluation costs and limited insight into proprietary models. Complementing this, Vidur-Agent is a trace-driven simulator designed to replay GAIATrace, enabling reproducible and low-cost system evaluation across diverse simulated environments. Together, these artifacts facilitate in-depth systems research, revealing how modern agentic systems behave and how various design choices shape their performance on complex, general tasks.

Key takeaway

For AI Scientists and Machine Learning Engineers evaluating multi-model agentic AI systems, GAIATrace and Vidur-Agent provide critical tools. You can now gain deep, reproducible insights into token-level reasoning and system-level behavior, overcoming the challenges of non-deterministic execution and high evaluation costs. Use these artifacts to characterize how design choices impact agent performance on general tasks, accelerating your research and development cycles with reliable simulation.

Key insights

GAIATrace and Vidur-Agent enable reproducible, low-cost characterization of complex multi-model agentic AI system behavior on general tasks.

Method

GAIATrace captures token-level traces from agentic systems (MiroThinker, OWL) on GAIA. Vidur-Agent then replays these traces for reproducible, low-cost system evaluation across diverse simulated environments.

In practice

Topics

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.