AGENTIF: Benchmarking Instruction Following of Large Language Models in
  Agentic Scenarios
Authors: name: Yunjia Qi, name: Hao Peng, name: Xiaozhi Wang, name: Amy Xin, name: Youfeng Liu, name: Bin Xu, name: Lei Hou, name: Juanzi Li

Large Language Models (LLMs) have demonstrated advanced capabilities in
real-world agentic applications. Growing research efforts aim to develop
LLM-based agents to address practical demands, introducing a new challenge:
agentic scenarios often involve lengthy instructions with complex constraints,
such as extended system prompts and detailed tool specifications. While
adherence to such instructions is crucial for agentic applications, whether
LLMs can reliably follow them remains underexplored. In this paper, we
introduce AgentIF, the first benchmark for systematically evaluating LLM
instruction following ability in agentic scenarios. AgentIF features three key
characteristics: (1) Realistic, constructed from 50 real-world agentic
applications. (2) Long, averaging 1,723 words with a maximum of 15,630 words.
(3) Complex, averaging 11.9 constraints per instruction, covering diverse
constraint types, such as tool specifications and condition constraints. To
construct AgentIF, we collect 707 human-annotated instructions across 50
agentic tasks from industrial application agents and open-source agentic
systems. For each instruction, we annotate the associated constraints and
corresponding evaluation metrics, including code-based evaluation, LLM-based
evaluation, and hybrid code-LLM evaluation. We use AgentIF to systematically
evaluate existing advanced LLMs. We observe that current models generally
perform poorly, especially in handling complex constraint structures and tool
specifications. We further conduct error analysis and analytical experiments on
instruction length and meta constraints, providing some findings about the
failure modes of existing LLMs. We have released the code and data to
facilitate future research.