AGENTIC-SYSTEMSApril 3, 20261 min read21 views

Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agents

OpenTools is a community-driven framework designed to standardize and enhance the reliability of tool-using AI agents by focusing on both invocation accuracy and intrinsic tool quality.

Computer Science > Artificial Intelligence

Title:Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agents

View PDF HTML (experimental)Abstract:Tool-integrated LLMs can retrieve, compute, and take real-world actions via external tools, but reliability remains a key bottleneck. We argue that failures stem from both tool-use accuracy (how well an agent invokes a tool) and intrinsic tool accuracy (the tool's own correctness), while most prior work emphasizes the former. We introduce OpenTools, a community-driven toolbox that standardizes tool schemas, provides lightweight plug-and-play wrappers, and evaluates tools with automated test suites and continuous monitoring. We also release a public web demo where users can run predefined agents and tools and contribute test cases, enabling reliability reports to evolve as tools change. OpenTools includes the core framework, an initial tool set, evaluation pipelines, and a contribution protocol. Experiments and evaluations show improved end-to-end reproducibility and task performance; community-contributed, higher-quality task-specific tools deliver 6%-22% relative gains over an existing toolbox across multiple agent architectures on downstream tasks and benchmarks, highlighting the importance of intrinsic tool accuracy.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Schema-Aware Localisation (SAL): Live Schema Grounding and Hallucination Validation for Oracle NL2SQL

Researchers introduce Schema-Aware Localisation (SAL), a lightweight middleware for Oracle NL2SQL that connects LLMs to live database catalogs. SAL dramatically improves SQL execution accuracy from 2.2% to 62.6% while drastically reducing hallucination-induced execution failures without requiring model retraining.

agentic-systems

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits

Researchers have proposed a loss-aware structured feature-map pruning framework for convolutional neural networks using multi-armed bandits. The approach significantly reduces computational complexity and storage costs while maintaining accuracy levels close to original unpruned models.

agentic-systems

SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs

Researchers have introduced SCAIR, a training-free agentic framework that enhances Knowledge Graph-based RAG (KG-RAG) for enterprise applications by injecting schema constraints into multi-hop iterative reasoning.

agentic-systems

Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents

Researchers have developed ScenarioGeneratorAgent, an automated pipeline that generates synthetic evaluation scenarios for Industry 4.0 AI agents. Tested on Smart Grid Transformers, the approach accelerates benchmark generation by 8x while maintaining high quality and adherence to IEC standards.

agentic-systems

Concept-based Visual Counterfactual Explanations with Diffusion Models

Researchers introduced C-VCE, a novel diffusion framework that generates human-interpretable visual counterfactual explanations by building concept bottleneck layers directly into the generative model.

agentic-systems

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

As LLMs tackle longer tasks, retaining complete interaction histories becomes contextually and computationally expensive. The ABBEL framework addresses this by isolating summaries into natural-language 'belief states' and supervising them via belief grading, recovering accuracy while significantly cutting memory usage.