arXiv:2511.09572v2 Announce Type: replace-cross Abstract: For agentic systems to use external tools to solve complex, long-horizon tasks, we need a large set of diverse and controllable tool-use environments. We introduce SynthTools, a fully LLM-based pipeline spanning the entire lifecycle: environment generation, simulation, validation and task construction. By operating end-to-end through LLMs, our framework complements other tool-use environments bottlenecked by the complexity of real APIs, and ensures scalability and controllability by design. The framework consists of three components: to
The rapid advancement in large language models (LLMs) has enabled new methodologies for generating and simulating complex environments, making this approach feasible now.
This development could significantly accelerate the creation and testing of advanced agentic systems, expanding their capabilities and applicability across various domains without being bottlenecked by real-world API complexities.
The development pathway for AI agents shifts towards more scalable and controllable synthetic environments, potentially lowering the barrier to entry and increasing the pace of innovation in agent development.
- · AI Agent Developers
- · LLM Providers
- · Software Development Tools
- · Traditional Tool-Use Environment Developers (non-LLM based)
More sophisticated and versatile AI agents are developed, capable of handling complex, long-horizon tasks.
The proliferation of advanced AI agents leads to increased automation across white-collar workflows and specialized industries.
The enhanced capabilities of AI agents begin to displace certain human expert roles, leading to shifts in workforce composition and economic value creation.
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