This talk explores how to overcome the context window limitations that prevent AI agents from accessing large APIs. It introduces "Codemode," a technique where agents write code against a typed SDK in a secure, sandboxed environment, moving beyond static tool definitions and enabling full API accessibility.
Sunil Pai from Cloudflare introduces "Code Mode," a paradigm where AI agents generate and execute code (like JavaScript) instead of using traditional JSON-based tool calling. This approach enables more efficient, stateful, and complex interactions with large-scale systems by leveraging the inherent capabilities of programming languages.
Sam Khavari, the creator of Pydantic, introduces Monty, a new, secure, and high-performance Python interpreter written in Rust. Monty is designed specifically for AI agents, bridging the gap between simple, limited tool-calling and complex, slow, full-featured sandboxes.
Mefta Sadat from Loblaw Digital discusses Alfred, an agentic orchestration layer designed to run AI shopping agents reliably in production. He covers the architecture built with LangGraph and GCP, the role of the Model Context Protocol (MCP) in simplifying API interaction, and practical MLOps strategies for observability, cost management, and ensuring reliability.
A panel discussion with experts from Arcade, Prosus Group, and MeaningStack who argue that most teams are building agents incorrectly. They dissect the failures of simple API wrappers, the pros and cons of MCP, and the critical role of governance and organizational structure in deploying agents successfully.
Sam Partee, CTO of Arcade AI, explains that building production-grade agentic systems requires moving beyond simple chatbots. He details the critical components for creating reliable, secure, and scalable tools, including rigorous schema management, the principle of least privilege, continuous evaluation, and a crucial distinction between 'exploratory' and 'operational' tools.