Vilnius, Lithuania, 4th June 2025, ZEX PR WIRE, Decodo, a leading platform for testing, launching, and scaling web data projects with proxy and scraping solutions, has introduced new tools and best practices to help companies scale data collection tailored for AI agent development.
AI agents, from customer support bots to autonomous research assistants, are only as effective as the data they consume. For fast-growing businesses, the challenge isn’t just building the model. It’s collecting and maintaining high-quality data in real-time.
Why scaling data collection matters
Most AI projects fail not due to modeling limitations, but because of poor data infrastructure. Training AI agents requires a continuous stream of structured, relevant data that evolves together with user behavior. Without scalable data pipelines, agents can quickly become outdated, biased, or unreliable.
“AI agents thrive on fresh, purpose-driven data,” says Vytautas Savickas, CEO at Decodo. “We’re helping users build solutions that don’t just collect more data, they collect better data.”
Gathering scalable AI-ready data
AI agents must serve a specific function, whether it’s generating insights, automating decisions, or interacting with users. For example, if an agent is meant to summarize news content, training it on generic product reviews will fail. Domain-specific, context-rich data is key here.
Data collection flows need to adapt to how the AI agent performs in production. Most tools need to integrate feedback loops that analyze irrelevant outputs and user corrections, feeding these back into the training pipeline to improve agent accuracy and resilience.
Manual data gathering can’t keep up with AI needs. Decodo proxy and scraping solutions enable ethical automation at scale. From extracting structured data from public web sources in real time to downloading videos from various platforms for AI training, the company helps businesses scale their data collection tools.
Further enhancing the tech stack
Solutions like Decodo’s recently introduced AI Parser that automatically presents easy-to-digest data help reduce technical overhead while enhancing data accuracy and ensuring faster integration into AI training workflows. Raw, unstructured web data is converted into clean, structured formats, making it usable for machine learning pipelines. With fewer preprocessing steps and streamlined data preparation, teams can allocate more resources to model development, experimentation, and scaling AI agents in various production environments.
Unlocking AI agent potential with smarter data infrastructure
As AI agents evolve from experimental tools to business-critical systems, the data collection infrastructure has to evolve with them. For businesses looking to unlock the full potential of their AI agents, scalable, intelligent data collection isn’t just an advantage – it’s a must. And various data collection solutions available in the market improve the AI agent training process.
About Us
Decodo (formely Smartproxy) is a leading provider of reliable and high-performance proxy and web scraping solutions for businesses, developers, and data-driven teams. With a global pool of over 120 million residential, mobile, and datacenter IPs, Decodo helps users collect public web data efficiently and anonymously at scale.
The Post How Decodo Helps to Scale Data Collection for AI Agents first appeared on ZEX PR Wire
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