Harbor: A Unified AI Agent Evaluation Framework Covering 80+ Benchmarks

As AI Agent technology advances rapidly, systematically evaluating agent capabilities has become a major industry challenge. A recent research paper from arXiv introduces Harbor Adapters and Harbor-Index, providing a unified infrastructure and high-quality meta-dataset for large-scale agent evaluation.

The Fragmentation Problem in AI Agent Evaluation

The core challenge facing AI agent evaluation today is benchmark fragmentation — every benchmark requires its own specific environment setup and integration approach, making cross-benchmark comparisons exceptionally difficult. Harbor Adapters addresses this by developing a unified adapter layer that successfully ports over 80 benchmarks into a single framework capable of evaluating any agent.

The research team applied rigorous code reviews and parallel experiments to validate adapter accuracy. This means developers no longer need to rebuild evaluation environments for each benchmark — a standardized interface handles testing across the board. The design philosophy mirrors the Adapter pattern in software engineering, abstracting heterogeneous systems into a unified interface.

Large-Scale Empirical Study: 8 Models × 54 Benchmarks

Building on the Harbor infrastructure, the research team conducted one of the largest agent capability evaluation experiments to date. They selected 8 models spanning different capability tiers and evaluated them across 54 benchmarks, with each model running on either Terminus-2 or one of three native harnesses.

This large-scale cross-evaluation revealed patterns that single-benchmark testing could never uncover:

• Different models show significant performance gaps on specific task types
• Certain failure modes appear consistently across models
• Even the strongest configuration (GPT-5.5 with Codex) achieved only a 28.0% pass rate on Harbor-Index

These findings suggest that current AI agent technology still has a considerable distance to travel before reaching general intelligence.

Harbor-Index: 82 Carefully Curated High-Quality Evaluation Tasks

Harbor-Index is the third core contribution of this research — a meta-dataset of 82 carefully curated, high-difficulty tasks spanning 29 benchmarks. Building this meta-dataset involved multiple rounds of rigorous filtering:

1. Difficulty filtering: Tasks that are too simple are excluded to ensure genuine challenges for current models
2. Dual AI and human review: A two-layer review mechanism guarantees the quality and evaluative relevance of every task
3. Review-and-repair iteration loop: Identified issues trigger continuous refinement until quality standards are met

The resulting Harbor-Index dramatically reduces operational costs while maintaining broad coverage and strong difficulty. Key metrics show that no evaluated model-harness configuration achieved a pass rate above 30% on Harbor-Index, clearly demonstrating the dataset's high discriminative power and practical value.

Fully Open-Sourced: Driving Industry Standardization for Agent Evaluation

The research team has released all adapters, evaluation results, in-depth analysis reports, and Harbor-Index as open-source resources. This open approach is expected to accelerate the standardization of agent evaluation methods.

For AI researchers and developers, Harbor delivers three concrete benefits:

• Lower evaluation barriers: No need to set up separate environments for each benchmark — a unified framework handles all testing
• Comparable results: The unified framework ensures fair comparisons across studies and teams
• High-quality benchmark reference: Harbor-Index can serve as a north-star metric for model development

Conclusion and Outlook

This work demonstrates that AI agent evaluation is evolving from scattered point-in-time tests toward systematic infrastructure. Harbor establishes a new evaluation paradigm for the field through three pillars: a unified adapter layer integrating 80+ benchmarks, large-scale cross-evaluation revealing capability gaps, and a high-quality meta-dataset providing a rigorous standard. As more benchmarks are incorporated into the Harbor ecosystem, the research community will gain a more comprehensive and reliable understanding of agent capabilities.