The Problem: Fragmentation in Agentic Benchmarking

Evaluating AI agents is notoriously difficult because existing benchmarks are highly heterogeneous. Different agent frameworks, environments, and task formats make it nearly impossible to compare performance across models or architectures. This lack of standardization leads to 'siloed' evaluation, where researchers must build custom wrappers for every new benchmark, slowing down iteration and making reproducibility a significant challenge.

Harbor Adapters: A Unified Interface

Harbor introduces a standardized adapter layer designed to decouple agent logic from specific benchmark environments. By implementing a common interface, Harbor Adapters allow developers to:

• Normalize Inputs/Outputs: Transform disparate benchmark formats into a consistent schema that any agent can consume.
• Reduce Boilerplate: Eliminate the need to write custom integration code for every new evaluation task.
• Enable Cross-Framework Testing: Facilitate the testing of various agent architectures (e.g., ReAct, Plan-and-Solve) against the same standardized environment without modifying the underlying agent code.

Harbor-Index: A Curated Meta-Dataset

Beyond the infrastructure, the authors provide the Harbor-Index, a curated meta-dataset designed to support large-scale evaluation. This index acts as a centralized repository of tasks, allowing researchers to:

• Scale Evaluation: Run agents against a diverse set of tasks that cover various reasoning, tool-use, and long-horizon planning capabilities.
• Ensure Consistency: Use a standardized set of metrics and ground-truth data to ensure that performance comparisons are apples-to-apples.
• Improve Reproducibility: By relying on a shared meta-dataset, the community can move away from ad-hoc evaluation sets, creating a more rigorous standard for what constitutes 'agentic capability.'