arXiv:2605.28000v1 Announce Type: cross Abstract: Large language model agents are increasingly expected to perform operational work: calling APIs, manipulating files, assembling workflows, and acting inside enterprise systems. Yet the tool layer on which this execution depends is still commonly treated as either a hand-written integration artifact or a static list of schemas exposed to a model. This paper introduces Tool Forge, a validation-carrying toolchain for converting natural-language capability intent into governed, sandbox-verified, cataloged tool artifacts and exposing those artifacts
The rapid advancement and deployment of large language models in enterprise settings necessitate improved governance and reliability for agentic execution.
This development addresses critical challenges in security, reliability, and control for AI agents, which are essential for their widespread adoption in operational roles.
The ability to convert natural-language intent into validated, governed tool artifacts provides a crucial layer of safety and predictability for AI agent interactions with enterprise systems.
- · AI platform providers
- · Enterprises adopting AI agents
- · AI agent developers
- · Cybersecurity firms
- · AI solutions with weak governance
- · Manual integration specialists
- · Organizations slow to adopt validated AI agent tooling
Enterprise adoption of AI agents accelerates due to increased trust and operational reliability.
New standards and regulatory frameworks emerge around AI agent governance and validation, driven by robust toolchains.
The development of AI agents becomes highly commoditized, shifting value creation to sophisticated validation and governance layers rather than just model development.
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Read at arXiv cs.AI