Nimble on Wednesday launched Web Search Agents, new agentic AI applications that autonomously learn customer use cases to scour the internet for relevant information that can be converted into curated data tables.
While analytics and AI tools are largely informed by an enterprise’s operational data, information from the web is also valuable when building applications that inform decisions and business processes. Operational data reveals what is happening within an organization, while web data shows what is taking place externally.
Together, the two provide richer context for agents and other decision-informing tools than one type of data alone.
“Web data is the world’s largest real-time signal network,” Michael Ni, an analyst at Constellation Research, told TechTarget. “Nimble allows enterprises to combine internal and external data, enabling AI to move from internally informed analysis to context-aware decisions.”
Use cases include real time competitive intelligence, such as dynamic pricing and product availability, examining market and sentiment signals, insight into competitors, and ensuring regulatory compliance.
Nimble’s Web Search Agents are designed to automate much of the research work that previously had to be performed by humans, building and maintaining complex web-scraping tools. In addition, Nimble’s agents are designed to self-learn a user’s domain to perform web data research with the accuracy of an expert, but with greater efficiency.
As a result, Web Search Agents are a potentially significant addition for Nimble customers, according to Donald Farmer, founder and principal of TreeHive Strategy.
“There has always been a bottleneck accessing web data because it meant building and maintaining brittle scraping scripts and managing proxies,” he told TechTarget. “Agents should autonomously … remove that bottleneck.”
Based in New York City, Nimble is a web data search specialist that raised $47 million in venture capital funding in February to fuel its growth. In addition to Nimble, vendors that specialize in enabling users to pull data from the web include Apify, Bright Data, Grepsr, MixRank and Oxylabs.
Aiding AI
Agents and other AI tools require reams of contextually relevant information to deliver accurate outputs at a high enough rate that they can be trusted in production.
Despite continually rising investments in AI development, more projects fail than succeed. Frequently, difficulties discovering and connecting agents with the context that enables them to perform properly prevent AI initiatives from moving beyond the pilot stage.
There has always been a bottleneck accessing web data because it meant building and maintaining brittle scraping scripts and managing proxies. Agents should autonomously … remove that bottleneck. Donald FarmerFounder and principal, TreeHive Strategy
With context critical to developing trustworthy AI tools, many vendors have introduced products in 2026 designed to help customers discover and operationalize relevant data. In June alone, AWS, Databricks, Microsoft and Snowflake unveiled new features aimed at connecting agents with context.
With Web Data Agents, Nimble is similarly providing capabilities aimed at helping customers feed agents with contextually relevant data, in a move motivated by user feedback, according to Uri Knorovich, the vendor’s co-founder and CEO.
“The feedback we kept hearing was simple — [customers’] agents could search the web, but they weren’t getting the specific data they needed. … Agents need web search that adapts to their specific task, context and objectives to optimize for accuracy and cost, not one-size-fits-all results,” he said.
Web Search Agents are built on customized search algorithms that enable them to learn autonomously, Knorovich continued.
“These advances allow search models to continuously learn a domain, optimize retrieval for a specific use case and deliver … more accurate web context than generic search engines,” he said.
Beyond accurately finding and retrieving relevant web data, Nimble’s Web Search Agents are designed to reduce the cost of searching the internet for task-specific context.
Using generic search engines that are task-agnostic can become expensive, according to Farmer, who noted that such tools often return massive, unstructured amounts of text that, when processed by an agent, consumes a high number of context window tokens.
In benchmark testing conducted by Nimble but not independently verified, Web Search Agents increased answer quality by over 20% and approximately halved the number of tokens spent on each query.
“A smart agent retrieving data should therefore be a great advantage,” Farmer said.
However, while valuable for Nimble customers, Web Search Agents are not the only such web data discovery and retrieval tools on the market, he continued.
For example, Grepsr and MixRank both offer AI-powered web scraping capabilities that can discover and deliver task-specific data. However, Farmer pointed out that there is slight differentiation, since Nimble’s Web Search Agents deploy headless browsers that pull the freshest version of a web page instead of a data cache.
“If you need that, it is a compelling feature,” Farmer said. “I don’t think there’s a lot here that is innovative compared to the rest of the market, but the work appears to be solid.”
Ni similarly noted that Nimble’s web data collection capabilities are not unique. Instead, the vendor has the potential to distinguish itself by combining live access to web data, agent-powered navigation and repeatable output into a managed external sensing layer, he continued.
“The proof point will be whether that delivers greater completeness and lower maintenance costs than traditional scraping platforms, vertical data platforms, or AI-native search APIs,” Ni said.
Next steps
While operational data remains the focal point for many enterprises, Nimble’s leadership believes that web data is becoming the primary source of context for agents, according to Knorovich.
With that in mind, Nimble plans to make it easier to connect an enterprise’s agents with web search by adding and improving self-learning and agent memory capabilities so Web Search Agents better understandtasks and get smarter with each use.
“The bigger vision is a web that agents can navigate as fluently as people do, and we intend to keep building toward that,” Knorovich said.
Ni noted that Nimble is positioning itself as an enterprise-grade layer for providing external context to agents. Going forward, its opportunity to continue serving existing users and attract new ones is by turning that layer into a control plane for external context.
“Parallel to the growth of internal context layers, enterprise leaders will want to know where any specific fact came from, how fresh and reliable it is, whether sources conflict, and whether an agent is permitted to act on it given a specific context,” Ni said. “Productizing context controls would expand Nimble’s relevance from developers to CDAOs and CAIOs.”
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
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