A deep dive into the architectural and economic foundations of memory for AI agents. The talk explores the core tradeoffs between classical data storage and dynamic agent behavior, introduces a human-inspired framework for memory, and discusses practical strategies and future directions for building reliable, evolving AI systems.
Anant Bhardwaj, CEO of Instabase, presents a pragmatic guide for building enterprise AI. He argues that AI agents are best used during the 'design-time' to create predictable workflows, rather than for autonomous 'runtime' operations. Bhardwaj also debunks the hype around RAG, highlighting its dependency on data quality, and explains why trust in AI systems stems from predictability, not just accuracy.
Will Bryk, co-founder and CEO of Exa, discusses the journey and technology behind building a search engine from scratch, specifically designed for AI systems. He explains why traditional search engines fail in an AI-first world and how Exa's full-stack, compute-heavy approach aims to solve the world's "information blocker" problems.
Anthropic's Boris Cherny, creator of Claude Code, discusses the paradigm shift from traditional coding to agentic workflows. He covers the co-evolution of models and their "harnesses," the importance of "hackability" via features like slash commands, and provides practical tips for leveraging coding agents for tasks of varying complexity.
Ryan Daniels and John Sarihan of Crosby discuss their innovative approach of building an AI-first law firm instead of a traditional legal software company. They detail how integrating lawyers and AI engineers creates a unique feedback loop for automating contract negotiations, moving from billable hours to per-document pricing to achieve deal velocity, and their vision for AI agents that can simulate entire negotiations.
Garrett Lord, CEO of Handshake, details the company's extraordinary pivot from a college career network to a dominant AI data provider. He explains how they leveraged their proprietary network of 500,000 PhDs and 3 million advanced degree holders to build a business on track to surpass $100 million ARR in its first year by providing high-quality, expert-generated data for training frontier AI models.