Large language models (LLMs) are impressive generalists, but they can get lost navigating the sprawling world of the web. Think of it like giving someone brilliant directions, but only vague ones like "go north." They might have the intelligence to figure it out, but a more precise roadmap would be much more efficient. This is the challenge addressed by a new research project called Auto-Intent. Researchers discovered that LLMs perform much better at web navigation when given a set of short, clear "intents." These intents act as mini-goals, guiding the LLM step-by-step through a task. Imagine trying to book a flight. Auto-Intent might provide hints like "selecting departure city," "choosing return date," or "specifying number of passengers." By presenting several of these intents at each step, the LLM can explore different paths and choose the most relevant one. This approach, termed "self-exploration," significantly boosted the performance of several LLMs, including GPT-4 and Llama variants, on challenging web navigation benchmarks like Mind2Web and WebArena. The system even demonstrated impressive cross-benchmark generalization, meaning an intent predictor trained on one dataset could improve performance on a completely different one. This opens exciting possibilities for improving LLM agents in diverse real-world scenarios where training data might be scarce. However, current research is limited to web navigation. The future could see Auto-Intent or similar techniques applied to broader domains, like controlling mobile apps or even physical robots, ushering in a new era of more capable and adaptable AI assistants.