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

Researchers introduce Agentick, a unified benchmark for evaluating diverse AI agents—from reinforcement learning to large language models—across 37 procedurally generated tasks. Testing 27 configurations reveals no single approach dominates, with GPT-4 mini leading overall while specialized methods excel in specific domains, suggesting significant optimization potential across all agent paradigms.

Agentick addresses a critical gap in AI research: the lack of standardized evaluation frameworks for comparing fundamentally different agent architectures. Previous benchmarks typically favored specific approaches, making it difficult to assess the relative strengths of RL agents trained from scratch versus foundation models leveraging pre-trained knowledge. This new benchmark democratizes comparison by providing a shared experimental ground with consistent metrics across observation modalities and task difficulty levels.

The benchmark's design reflects emerging trends in AI development where hybrid approaches increasingly dominate. The finding that GPT-4 mini leads overall performance while PPO excels at planning tasks demonstrates that agent selection depends heavily on problem structure rather than algorithmic superiority. This nuance matters because it suggests the field should move beyond monolithic "best" approaches toward domain-specific agent selection frameworks.

For the broader AI ecosystem, Agentick functions as both evaluation infrastructure and training ground. The inclusion of pre-built supervised fine-tuning datasets and oracle reference policies accelerates research velocity for teams lacking resources to generate their own training data. The live leaderboard creates competitive incentives for continuous improvement, similar to how ImageNet catalyzed computer vision progress.

The revelation that ASCII observations outperform natural language contradicts conventional wisdom and suggests researchers may be over-engineering observation spaces. This finding has practical implications for deployment, as ASCII-based representations reduce computational overhead while improving performance. Moving forward, the benchmark's impact depends on adoption rates among major research institutions and whether findings translate into practical improvements in real-world autonomous systems.

• →No single agent paradigm dominates across all task types, indicating diverse optimization opportunities remain across RL, LLM, and hybrid architectures.
• →GPT-4 mini achieves highest overall performance while specialized algorithms like PPO excel in specific domains such as planning and multi-agent tasks.
• →Reasoning harnesses amplify LLM performance by 3-10x, suggesting prompt engineering and chain-of-thought approaches significantly impact foundation model effectiveness.
• →ASCII observations consistently outperform natural language inputs, challenging assumptions about human-interpretable observation spaces in agent design.
• →Agentick provides standardized evaluation infrastructure with pre-built datasets and oracle policies, reducing barriers to entry for foundation model post-training research.