Building Search for AI Agents with Exa CEO Will Bryk

2026-06-06 · Source: The a16z Show · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

Exa CEO Will Bryk discusses building a search engine specifically for AI agents, contrasting it with traditional human-centric platforms like Google. Exa, founded around 2021, utilizes advanced Transformer models to provide deeper context, comprehensive results (thousands to tens of thousands), and highly customizable search capabilities for complex queries. Bryk explains that while human click data is less relevant for agents, the demand for extreme reliability (99.9999% quality) in business-critical applications makes agentic search uniquely challenging. Exa's approach helps solve the "tokenpocalypse" by enabling smaller, tool-using LLMs, potentially saving customers 20x on costs. He predicts agentic search will surpass Google search in business size by the 2030s, driven by the sheer volume of agent interactions, and identifies infrastructure, data accumulation, and advanced retrieval over quadrillions of pages as key future bottlenecks.

Key takeaway

For AI Architects and Engineers building agentic systems, recognize that traditional search engines are inadequate for your agents' needs. You should prioritize search solutions designed for comprehensive, customizable, and high-fidelity information retrieval to ensure agent accuracy and efficiency. Adopting retrieval-augmented generation with smaller, hyper-intelligent models can significantly reduce token consumption and operational costs, making your agent deployments more scalable and reliable.

Key insights

AI agents demand a new search paradigm focused on comprehensive, controllable, and deep information retrieval, distinct from human-optimized engines.

Principles

• AI agents require comprehensive, controllable search.
• LLMs enable new, efficient search architectures.
• Agentic search demands extreme reliability (99.9999%).

Method

Exa builds search from scratch, optimizing for complex semantic/keyword queries and comprehensive results, using pre/post-training of embedding models and RL on search tools.

In practice

• Employ retrieval with smaller LLMs to cut token costs.
• Conduct A/B tests for agentic search performance.
• Prioritize comprehensive company and people search.

Topics

• AI Agents
• Agentic Search
• Information Retrieval
• LLM Cost Optimization
• Vector Databases
• Exa

Best for: CTO, VP of Engineering/Data, Machine Learning Engineer, AI Engineer, AI Architect, Director of AI/ML

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• Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search — Budgeting search depth and retrieval quality offers more accuracy gains than expanding token limits for agentic RAG.
• Agents need vector search more than RAG ever did — AI agents significantly increase retrieval demands, making purpose-built vector search infrastructure more critical, not less.
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Editorial summary, takeaway, and curation by AIssential. Original article published by The a16z Show.