Tool Forge: A Validation-Carrying Toolchain for Governed Agentic Execution
On the map: LLM agents and tool use → Tool learning and function calling
Abstract (arXiv)
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 to agents through a token-efficient routing layer. Tool Forge treats a tool as a capsule containing intent, capability contract, implementation, dependency policy, tests, documentation, runtime validation evidence, lifecycle state, credential bindings, and routing metadata. It also introduces a Router that exposes intent-scoped tool sessions instead of loading full catalog schemas into the model context. We describe the system architecture, validation pipeline, MCP-facing routing model, governance controls, and initial reproducible benchmarks from the open-source implementation. Across 83 Router benchmark cases, Tool Forge Router achieves aggregate micro-F1 of 0.901 while reducing estimated task-flow tool context by 99.2% relative to naive full-catalog schema exposure. In a 25-case end-to-end generation probe over local-tool tasks, Tool Forge generates 25 of 25 tool bundles, reaches micro-F1 of 0.940 against deterministic acceptance checks, and passes 23 of 25 live sandbox validations. These results are presented as an initial systems benchmark, not as a state-of-the-art claim. The paper identifies remaining challenges in adversarial routing, broader API grounding, sandbox isolation, and cross-system evaluation.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 6 of its 30 references that are in the map. First posted on arXiv 27 May 2026.
Builds on
6 of 30 references.
Closest ancestors · 2
Same direction, newest first
- 2023Gorilla: Large Language Model Connected with Massive APIs24 May
- 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb
Other references · 4
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2026Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agentscited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2025AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of Tool Orchestration
- 2026ChromaFlow: A Negative Ablation Study of Orchestration Overhead in Tool-Augmented Agent Evaluation
- 2026Formal Verification of Agentic Systems over Operational Data
- 2025ToolFuzz -- Automated Agent Tool Testing
- 2026MANTRA: Synthesizing SMT-Validated Compliance Benchmarks for Tool-Using LLM Agents
- 2026AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents
- 2026Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction
- 2026E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios