Large Language Models (LLMs) have demonstrated impressive capabilities in tasks such as code generation, completion, and repair. A recent trend involves designing agentic systems, where multiple LLM-powered agents collaborate to accomplish software engineering goals. These multi-agent setups have achieved state-of-the-art performance in several tasks, but their practical adoption faces significant challenges. In this tutorial, we will begin by introducing the foundations of agentic systems and their applications in software engineering. We will then broaden the discussion to cover critical aspects of code intelligence using LLMs and agentic systems that must be addressed for real-world deployment, including code comprehension, effective communication, security concerns, explainability, reasoning capabilities, and computational efficiency. Through this lens, we will analyze the current limitations of LLMs and agent-LLMs, highlighting gaps between academic progress and practical use. We will conclude with an interactive discussion of open research challenges and future directions. Attendees will leave with a clear understanding of the strengths and limitations of current LLM-based tools, as well as strategies for improving the reliability, efficiency, and usability of these systems in real-world software development workflows.

Target Audience: This tutorial is designed for researchers, practitioners, and graduate students interested in the intersection of AI and software engineering. Participants should have a basic familiarity with LLMs and AI concepts, though no advanced background is required. The tutorial will be particularly relevant for those exploring applications of AI in code intelligence, program repair, and software development workflows. Attendees with prior exposure to programming or LLM-based tools will benefit most, but the content is accessible to anyone with general knowledge of machine learning or software systems. The audience can expect to gain both conceptual understanding and research insights into agentic-AI systems.