The field of agentic systems and language agents is rapidly evolving, with a focus on developing more reliable, efficient, and generalizable models. Recent research has highlighted the importance of matching train-test environments for building reliable real-world code-fixing agents and the need for more robust evaluation benchmarks. The introduction of new frameworks and tools, such as CoreThink Agentic Reasoner and Lanser-CLI, has improved the performance and generalization of language agents across diverse domains. Additionally, the development of self-evolving agents, such as ALITA-G, has shown promise in transforming general-purpose agents into domain experts. Noteworthy papers include: Agentic Reinforcement Learning for Real-World Code Repair, which introduced a scalable simplified pipeline for large-scale reinforcement learning and achieved significant gains in code-fixing performance. On Generalization in Agentic Tool Calling, which presented the CoreThink Agentic Reasoner framework and achieved state-of-the-art performance on multiple tool-calling benchmarks. The Tool Decathlon, which introduced a benchmark for language agents offering diverse Apps and tools, realistic environment setup, and reliable execution-based evaluation.