Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge.Yet existing agentic frameworks treat external information as unstructured text and fail to leverage the topological dependencies inherent in real-world data.To bridge this gap, we introduce Agentic Graph Learning (AGL), a paradigm that reframes graph learning as an interleaved process of topology-aware navigation and LLM-based inference.Specifically, we propose AgentGL, the first reinforcement learning (RL)-driven framework for AGL.AgentGL equips an LLM agent with graphnative tools for multi-scale exploration, regulates tool usage via search-constrained thinking to balance accuracy and efficiency, and employs a graph-conditioned curriculum RL strategy to stabilize long-horizon policy learning without step-wise supervision.Across diverse Text-Attributed Graph (TAG) benchmarks and multiple LLM backbones, AgentGL substantially outperforms strong GraphLLMs and GraphRAG baselines, achieving absolute improvements of up to 17.5% in node classification and 28.4% in link prediction.These results demonstrate that AGL is a promising frontier for enabling LLMs to autonomously navigate and reason over complex relational environments.The code is publicly available at https://github.com/sunyuanfu/AgentGL.