Ryan Ahern of Exa presents Exa Snapshot, a point-in-time web search that lets agents query the web as it appeared on any past date, and shows how teams can backtest predictive signals and financial models without overfitting.
Ryan Ahern of Exa explains how version-controlled search through Exa Snapshot lets you view the web as it existed on a past date, enabling backtests of AI agents while avoiding look-ahead bias. A hands-on acquisition-prediction exercise illustrates feature tuning and the dangers of overfitting without holdout sets.
Jakub Hojsan, a forward deployed engineer at Exa, explains how search built for coding and code-review agents closes the gap left by model knowledge cutoffs. He covers why agents need explicit rules for when to search, how query-dependent highlights cut a 100,000-character page to about 500 characters without added latency, and how Exa compares with native web search on transparency, cost, and model-provider independence, with a preview of Exa Agent.
Jeffrey Wang, co-founder of Exa, details how "go to market" (GTM) is transforming into an AI engineering problem. He showcases Exa's agent-first approach, using tools like an ICP dashboard for market intelligence and a personal AI clone (Jeffbot) to automate and optimize sales, emphasizing the need for robust APIs and arbitrarily customizable systems in this new AI-driven landscape.
Will Bryk, CEO of Exa, explains why traditional keyword-based search is insufficient for AI agents and introduces a new paradigm of neural, semantic search. He demonstrates how a hybrid approach, combining neural for discovery and keyword for precision, enables AI agents to perform complex, multi-step information retrieval tasks that were previously impossible.