With the in-depth application of artificial intelligence (AI) in the fields of business, scientific research and technology, the importance of data has been magnified unprecedentedly. To make AI truly “smart”, it must first be able to see widely, listen more, and understand deeply — this is inseparable from the capture, cleaning and processing of massive data. In this process, proxy technology is playing an indispensable role behind the scenes.
Whether it is the web corpus required for training large models, or real-time capture of dynamic content such as social media comments, news information, and product information, AI’s data needs are high-frequency, large-scale, and widely distributed. If a request is initiated directly from the target website, it is easy to be restricted due to too many requests or frequent access. At this time, a high-quality proxy network becomes an important bridge connecting the AI system and the network world.
Taking residential proxies and ISP rotating proxies as examples, they can provide AI systems with more stable and real access paths, simulate user behaviors from different regions around the world, break through geographical restrictions, and bypass blocking mechanisms. When crawling data across countries, languages, and platforms, AI models can also ensure a higher access success rate and content coverage.
Furthermore, many AI projects are no longer limited to a single crawler program, but use multi-threaded and distributed architectures to crawl multiple sources at the same time. This places higher demands on the concurrency and connection stability of the agent. At this time, an agent solution that supports unlimited concurrency, precise positioning, and long-term sessions can greatly improve the efficiency and integrity of data acquisition.
In addition, with mechanisms such as sub-account permission management and IP whitelists, the agent solution can also help the AI data team better isolate and schedule resources, prevent task conflicts or leaks, and improve the security and transparency of the overall system.
It can be said that on the road to industrialization of AI applications, intelligent agent services are gradually becoming part of the data infrastructure. Agents not only silently support data collection behind the scenes, but also serve as a catalyst for AI to achieve intelligent decision-making in various vertical industries.
In the picture of the intelligent future, whoever masters the ability to acquire data efficiently, safely, and stably will master the underlying competitiveness of building a more powerful AI system.