The field of artificial intelligence is witnessing significant developments in the realm of autonomous agents and tool-integrated reasoning. Recent research has focused on enhancing the capabilities of large language models (LLMs) to interact with external tools and perform complex tasks. This has led to the creation of more sophisticated agents that can adapt to new environments and learn from experience. Notably, the integration of reinforcement learning and graph-based planning has enabled agents to efficiently utilize tools and achieve substantial improvements in task accuracy and execution efficiency. Furthermore, the development of novel frameworks and architectures has facilitated the training of agents that can generalize across diverse tasks and environments. Overall, these advancements are paving the way for more capable and autonomous AI systems. Noteworthy papers include: DeepAgent, which introduces an end-to-end deep reasoning agent that performs autonomous thinking and tool discovery, and PORTool, which proposes a reinforcement learning method that encourages a tool-use LLM to explore various trajectories yielding the correct answer.