Agentic Search Builds Answers, Not Just Finds Pages

Agentic search aims to assemble direct answers, not just find pages, by tackling huge challenges in data quality and custom infrastructure.

A new class of search technology is emerging that doesn't just find web pages—it synthesizes direct answers for AI agents. In a recent Stack Overflow Blog interview, Jeff Wang, co-founder of the search platform EXA, explained the concept of "agentic search." Unlike traditional search engines that return a list of links for a human to interpret, an agentic search engine processes a query, scours the web, and constructs a coherent, trustworthy answer that an autonomous AI agent can use to take action. This shift from information retrieval to answer assembly requires a complete reimagining of the underlying infrastructure, moving far beyond keyword indexing to tackle problems of meaning, scale, and data quality across the entire internet.

The technical hurdles to building such a system are immense. According to Wang, existing tools are often insufficient for the task. The core of agentic search relies on embeddings—numerical representations of content that capture its semantic meaning. To operate at web scale and provide relevant results for complex agentic queries, EXA had to develop its own custom embeddings from scratch. Furthermore, storing and querying these embeddings for trillions of potential interactions demanded a custom-built vector database. Off-the-shelf solutions couldn't provide the necessary performance and scalability. Building this foundational technology in-house allows for deep optimization, ensuring the system can handle massive query loads while delivering high-quality, synthesized information in real-time.

This development fits into a broader trend where AI systems are evolving from simple chatbots into sophisticated, autonomous agents capable of performing multi-step tasks. While techniques like Retrieval-Augmented Generation (RAG) allow large language models to pull in external information to ground their responses, agentic search operates on a much grander scale. It aims to be the definitive information source for any agent needing to interact with the web. A critical challenge highlighted by Wang is the sheer volume of redundant and low-quality content online. A huge part of building a reliable agentic search engine involves creating a robust data pipeline to deduplicate and clean the web, establishing a high-trust dataset that agents can depend on for accurate information without constant human supervision.

For developers, founders, and CTOs, the rise of agentic search signals that the next major frontier in AI is not just about building more powerful models, but also about creating the specialized data infrastructure that feeds them. The ability to process, clean, and serve web-scale data in a format that AI agents can reliably use is becoming a significant competitive moat. This creates a high barrier to entry, as the engineering effort and capital required are substantial. The key thing to watch is how this technology evolves. Will it become a foundational utility that powers a new generation of autonomous applications, or will it remain a specialized tool for high-end AI systems? The answer will determine the pace at which truly capable AI agents become a mainstream reality.

For engineers and CTOs, agentic search represents a paradigm shift beyond traditional keyword-based retrieval. Understanding its infrastructure challenges—like custom embeddings and vector database performance—is crucial for building or integrating next-generation AI products that rely on accurate, synthesized information from the web.

Companies building AI agents or relying on web data face a new competitive landscape. The high cost and complexity of developing agentic search infrastructure create a significant barrier to entry, favoring well-funded players who can solve data quality and scaling problems to offer superior, answer-driven products.