Nimble today launched Web Search Agents, a specialized web research tool that adapts to each customer's domain rather than returning generic results, reducing token consumption by 51% per query while improving answer quality by 21 points.
The product targets enterprises running long-running AI agents for business-critical research, where accuracy and cost efficiency matter more than speed.
It is available immediately through multiple developer interfaces with a free trial.
What happened
Nimble launched Web Search Agents, a product that learns a customer's domain and runs complex web research tasks autonomously. Benchmark testing showed a 21-point increase in answer quality and 51% fewer tokens spent per query.
Why it matters
AI agents using generic web search waste tokens processing irrelevant results. Nimble's task-specific approach combines proprietary indexes with real-time retrieval to reduce both token costs and the manual work teams must redo, making it valuable for enterprises where accuracy and cost are critical bottlenecks.
What to watch
Web Search Agents is available via API, SDK, and Model Context Protocol with a free trial. Nimble reports fielding more than 90 million searches a day across a customer base that includes Fortune 500 companies and AI-native startups; Rox reported a 20-fold reduction in token costs after adoption.
Ask the AI about this article →
Nimble's Web Search Agents addresses a real cost and quality problem in production AI agents. Generic web search tools return broad, unstructured results that force agents to process irrelevant pages and make redundant tool calls—a pattern that burns tokens and slows inference. By learning domain-specific requirements and tuning retrieval strategies to match the task, Nimble's harness reduces both the token overhead and the post-processing burden on engineering teams. The benchmark results—51% fewer tokens and a 21-point quality gain—suggest the approach works at scale; Rox's reported 20-fold token reduction after adoption indicates the gains are not theoretical.
The timing reflects a maturing market concern. As enterprises deploy AI agents for business-critical research (market research, lead enrichment, competitive intelligence), cost and accuracy have become the bottleneck, not speed. Nimble explicitly targets teams running agents that operate for hours, where a missed source has higher cost than a slower answer. The company's positioning—specialized agents for specialized tasks—also maps to feedback from customers like Qodo, whose teams value the ability to tune agents to surface only relevant signals rather than generic summaries.
For example, today's edition would include:
AI-summarized, only the topics you pick — one digest a day via Email, Slack, or Discord.
Free · takes 30 seconds · unsubscribe anytimeWhat is AIToday? →
Ask AI anything about this article. Q&As are published on this page for other readers too.
Israeli startup DataAgent Ltd
SK Hynix presented a custom HBM concept at SEMICON Taiwan 2026, where compute functions are placed in the base…
Taoyuan is positioning itself as a northern hub for AI data centers (AIDC), citing the Tatan area and an LNG c…
The U.S. Department of Defense announced on August 31 that it has deployed ChatGPT Mil, a customized version o…
Nvidia reported earnings that were both remarkable and boring, reflecting its focus on avoiding a consolidated…
Anthropic has agreed to a $35bn cloud-computing contract with Lambda, a Nvidia-backed cloud provider
