Tool calling

⚡️Monty: the ultrafast Python interpreter by Agents for Agents — Samuel Colvin, Pydantic

⚡️Monty: the ultrafast Python interpreter by Agents for Agents — Samuel Colvin, Pydantic

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.

Building an Orchestration Layer for Agentic Commerce at Loblaws

Building an Orchestration Layer for Agentic Commerce at Loblaws

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.

Tool Calling

Tool Calling

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.

Building Agentic Tools for Production // Sam Partee

Building Agentic Tools for Production // Sam Partee

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.

How Claude Code Works - Jared Zoneraich, PromptLayer

How Claude Code Works - Jared Zoneraich, PromptLayer

An unofficial deep dive into the architecture of modern coding agents like Claude Code. Jared Zoneraich of PromptLayer explains the shift towards simpler, model-centric designs, detailing the core components like the master loop, tool calling (especially `bash`), and context management strategies. The talk also contrasts Claude's philosophy with other agents like Codex, AMP, and Cursor, offering practical takeaways for building your own AI agents.

Production Ready AI Agents

Production Ready AI Agents

Sam Partee, CTO of Arcade, explains the critical gap between AI agents that gather context and those that take secure, real-world actions. He introduces Arcade as a middleware platform that solves complex challenges like user authorization, fine-grained permissions, and token management, enabling developers to build scalable, enterprise-ready agents.