RT Avi Chawla (@_avichawla)RAG is a distraction!Here's how Google and Microsoft actually give context to their production agents:To understand this, think about what "give an agent context" actually means in production.In production, data lives across Slack, Gmail, Jira, Drive, Salesforce, GitHub, and SQL databases. Each source has different auth, different data formats, different update cycles.A query like "summarize all activity on the auth migration this week" needs to pull from five sources simultaneous ...

RAG is a distraction!Here's how Google and Microsoft actually give context to their production agents:To understand this, think about what "give an agent context" actually means in production.In production, data lives across Slack, Gmail, Jira, Drive, Salesforce, GitHub, and SQL databases. Each source has different auth, different data formats, different update cycles.A query like "summarize all activity on the auth migration this week" needs to pull from five sources simultaneously, filter by time, check p ...

Core Problem & Solution - The presentation introduces Exa, a search engine designed for AI, addressing the limitations of traditional search engines built for human users [5][23] - Exa aims to provide an API that delivers any information from the web, catering to the specific needs of AI systems [22][41] - Exa uses transformer-based embeddings to represent documents, capturing meaning and context beyond keywords [11][12] AI vs Human Search - Traditional search engines are optimized for humans who use simple queries and want a few relevant links, while AIs require complex queries, vast amounts of knowledge, and precise, controllable information [23][24] - AI agents need search engines that can handle multi-paragraph queries, search with extensive context, and provide comprehensive knowledge [31][32][33] - Exa offers features like adjustable result numbers (10, 100, 1000), date ranges, and domain-specific searches, giving AI systems full control [44] Market Positioning & Technology - Exa launched in November 2022 and gained traction for its ability to handle complex queries that traditional search engines struggle with [15] - The company recognized the need for AI-driven search after the emergence of ChatGPT, realizing that LLMs need external knowledge sources [17][18] - Exa combines neural and keyword search methods to provide comprehensive results, allowing agents to use different search types based on the query [47][48] Future Development - Exa is developing a "research endpoint" that uses multiple searches and LLM calls to generate detailed reports and structured outputs [51] - The company envisions a future where AI agents have full access to the world's information through a versatile search API [48] - Exa aims to handle a wider range of queries, including semantic and complex ones, turning the web into a controllable database for AI systems [38][39][40]