Agentick: A Unified Benchmark for General Sequential Decision-Making Agents

• URL: http://arxiv.org/abs/2605.06869v2
• Date: Tue, 12 May 2026 18:33:33 GMT
• Title: Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
• Abstract summary: Agentick is a benchmark for sequential decision-making agents.<n>It provides 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities.<n>An evaluation spanning 27 configurations and over 90,000 episodes reveals that no single approach dominates.
• Score: 30.028388632526745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI agent research spans a wide spectrum: from RL agents that learn from scratch to foundation model agents that leverage pre-trained knowledge, yet no unified benchmark enables fair comparison across these approaches. We present Agentick, a benchmark for sequential decision-making agents designed to evaluate RL, LLM, VLM, hybrid, and human agents on common ground and to power research on the fundamental challenges of sequential decision-making. Agentick provides 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities, all exposed through a single Gymnasium-compatible interface. The benchmark ships with a Coding API, oracle reference policies for all tasks, pre-built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation spanning 27 configurations and over 90,000 episodes reveals that no single approach dominates: GPT-5 mini leads overall at 0.309 oracle-normalized score while PPO dominates planning and multi-agent tasks; the reasoning harness multiplies LLM performance by 3-10x; and ASCII observations consistently outperform natural language. These findings highlight the substantial room for improvement that remains across all agent paradigms. Agentick's capability-decomposed, multi-modal design provides the empirical infrastructure needed to drive progress toward general autonomous agents, both as an evaluation framework and as a training ground for RL post-training of foundation models in truly sequential environments.

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