AI systems are only as good as the context they see. When they rely on shallow or outdated web data, you get hallucinations, blind spots, and fragile decisions. In this session, Uri will show how Nimble’s Web Search Agents, combined with LangChain, help leaders close critical “intelligence gaps” by bringing high-quality, fresh, and defensible web data into their AI stack; improving reliability, trust, and business impact.

We’ll focus on practical context engineering patterns rather than low-level code: where web agents sit in your architecture, how they enrich internal data, and how to govern and observe AI that depends on live information from the web. Attendees will walk away with concrete design patterns, example use cases, and a simple framework for deciding when and how to use web search agents to make AI applications more robust and context-aware.