Imagine having a digital assistant that not only understands your commands but can also figure out how to achieve complex goals in various environments, from navigating virtual worlds to booking your next vacation. This is the promise of goal-directed interactive agents. Recent advances in large language models (LLMs) have opened up exciting possibilities for creating such agents. A new research survey explores the complex tasks these agents are tackling and what’s holding them back from truly seamless integration into our lives. The survey emphasizes the challenges of translating vague user requests into concrete steps. For example, an agent needs to break down a request like "Plan my trip to Hawaii" into smaller actions: searching for flights, finding hotels, and arranging activities. Different tasks come with unique challenges. Navigating a 3D game world, like Minecraft, requires an agent to process visual information and make decisions in a dynamic environment. In contrast, booking a flight online involves understanding website structures, filling out forms, and making choices based on various criteria. Other tasks, like question answering, involve extracting information from various sources like Wikipedia or knowledge bases. The research highlights key aspects impacting agent performance: the explicitness of goal specifications, the type of goal (reaching a world state vs. answering a question), and the action space (mouse clicks, API calls, code execution). How well an agent observes the environment—whether it has complete or partial information—plays a crucial role. So does feedback on actions, including intermediate rewards that tell the agent how well it's progressing. The survey identifies the need for more robust evaluation methods that go beyond simple success/failure metrics. For instance, some tasks require subjective evaluation, such as judging the quality of a generated image or the “funniness” of a joke. Ultimately, the future of goal-directed agents depends on better user interaction, allowing the agent to clarify vague requests and adapt to user preferences throughout the task. More refined testing will be key to pinpointing agent weaknesses, and standardized benchmarks will enable researchers to reliably compare different agent designs. As AI agents become more sophisticated, the research aims to push beyond simple commands and toward a world where agents truly understand and execute complex goals, acting as partners in achieving our aims.