Tool Forge: A Validation-Carrying Toolchain for Governed Agentic Execution

Tool Forge presents a validation-carrying toolchain that converts natural-language descriptions into governed, sandbox-verified tools for large language model agents. The system achieves 99.2% reduction in context requirements while maintaining 0.940 micro-F1 accuracy, addressing critical infrastructure gaps in enterprise agentic execution.

Tool Forge addresses a fundamental infrastructure challenge in AI systems: how to safely and efficiently enable language model agents to interact with external tools and APIs. Currently, most implementations rely on static schema exposure or hand-written integrations, creating scalability and governance bottlenecks. This research presents a more sophisticated approach that treats tools as comprehensive capsules containing not just capability definitions but also validation evidence, dependency policies, credential bindings, and lifecycle state management.

The emergence of agentic AI systems operating within enterprise environments has created urgent demand for better tool infrastructure. As models increasingly orchestrate real-world operations—calling APIs, manipulating files, executing workflows—the security and governance implications become critical. Tool Forge's validation pipeline and sandbox verification mechanisms represent essential safeguards that move beyond reactive error handling to proactive capability certification.

The technical achievements are substantial: a 99.2% reduction in context overhead through intent-scoped routing versus full-catalog exposure is operationally significant for cost and latency. The 0.940 micro-F1 score on live sandbox validations demonstrates practical viability, though the authors appropriately avoid claiming state-of-the-art performance.

For the broader AI infrastructure ecosystem, this work highlights the gap between model capabilities and production-ready deployment frameworks. Enterprise adoption of AI agents requires robust governance layers that this system attempts to provide. The open-source implementation enables broader community validation and iteration. Key remaining challenges—adversarial routing robustness, cross-system evaluation, and sandbox isolation guarantees—suggest this represents incremental progress rather than a complete solution.

• →Tool Forge reduces tool context overhead by 99.2% through intent-scoped routing instead of exposing full tool catalogs to models.
• →The system treats tools as validated capsules containing intent, contracts, tests, and governance metadata rather than static schemas.
• →Achieves 0.940 micro-F1 accuracy on sandbox validations across 25 end-to-end test cases with open-source reproducible benchmarks.
• →Addresses critical infrastructure gaps for enterprise agentic AI systems operating with external APIs and file manipulation.
• →Identifies remaining challenges in adversarial routing, API grounding, and cross-system evaluation limiting current production readiness.