# FABRIC: Framework for Agent-Based Realistic Intelligence Creation

arXiv: https://arxiv.org/abs/2510.17995 · HTML: https://mlsanity.com/paper/2510.17995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 15 of 15)

- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [LESS: Selecting Influential Data for Targeted Instruction Tuning](https://mlsanity.com/paper/2402.04333/) (2024)
- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [Language Models as Agent Models](https://mlsanity.com/paper/2212.01681/) (2022)
- [Agentic Large Language Models, a survey](https://mlsanity.com/paper/2503.23037/) (2025)
- [On the Out-Of-Distribution Generalization of Multimodal Large Language Models](https://mlsanity.com/paper/2402.06599/) (2024)
- [Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data](https://mlsanity.com/paper/2304.01196/) (2023)
- [Mixture cure semiparametric additive hazard models under partly interval censoring -- a penalized likelihood approach](https://mlsanity.com/paper/2401.01234/) (2024)
- [ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP](https://mlsanity.com/paper/2308.02537/) (2023)
- [The observational partial order of causal structures with latent variables](https://mlsanity.com/paper/2502.07891/) (2025)
- [SyGra: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data](https://mlsanity.com/paper/2508.15432/) (2025)
- [Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space -- Transformer Ensemble Models Tackling Deception and Persuasion](https://mlsanity.com/paper/2311.18730/) (2023)
- [Navigating Knowledge Management Implementation Success in Government Organizations: A type-2 fuzzy approach](https://mlsanity.com/paper/2406.12345/) (2024)

## Built on by (most cited first, 1 of 1)

- [Agent Seer: Synthesizing Scenarios from Specification Understanding](https://mlsanity.com/paper/2608.26133/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly deployed as agents, expected to decompose goals, invoke tools, and verify results in dynamic environments. Realizing these capabilities requires access to agentic data-structured interaction records that couple user intents with tool specifications, argument-grounded calls, and verifiable execution traces. However, collecting such data from human annotators is costly, time-consuming, and difficult to scale. We present a unified framework for synthesizing agentic data using only LLMs, without any human-in-the-loop supervision. This framework decomposes generation into modular pipelines that produce complete interaction records spanning task specifications, tool definitions, policy pseudocode, natural language exchanges, and execution traces. Records conform to strict syntactic and semantic constraints, ensuring machine-parseability and faithful alignment across inputs, outputs, and tool calls. Beyond single tasks, there is support for both multi-task and multi-turn agent interactions, enabling the construction of datasets that reflect the full spectrum of tool-use competencies. To ensure quality and consistency, the framework integrates constrained generation formats, JSON-schema validation, and judge-based filtering. This paper formalizes the schema for agentic records, details the prompt design principles that guide generation, and introduces scalable pipelines for high-quality synthetic data. By providing a reproducible, LLM-only alternative to manual collection, hence advancing the development of agentic LLMs capable of robust tool use.