Multi agent systems

Emmett Shear on Building AI That Actually Cares: Beyond Control and Steering

Emmett Shear on Building AI That Actually Cares: Beyond Control and Steering

Emmett Shear, founder of Twitch and former OpenAI interim CEO, presents a new paradigm for AI alignment called "organic alignment." He argues that the prevalent "steering and control" model is fundamentally flawed, potentially leading to disaster. Shear advocates for developing AI systems that learn to genuinely care about humans, treating alignment as a continuous process rather than a fixed state.

Build and monitor multi-agent contact centers using Weights & Biases

Build and monitor multi-agent contact centers using Weights & Biases

This post explores the shift from costly legacy contact center software to multi-agent AI systems. It demonstrates how to build, monitor, and evaluate these complex agentic systems using the Weights & Biases AI Developer Platform, with a focus on tracing, quality assessment, and ensuring consistent customer support.

A2A:The Agent-to-Agent Protocol

A2A:The Agent-to-Agent Protocol

Heiko Hotz and Sokratis Kartakis of Google Cloud introduce the Agent-to-Agent (A2A) protocol, a new open standard for enabling stateful, secure, and asynchronous collaboration between AI agents built on different frameworks. They contrast it with tool-use protocols like MCP and discuss its microservices-like architectural benefits.

Underwriting Assist - A Multi Agent System // Somya Rai | Maria Zhang // Agents in Production 2025

Underwriting Assist - A Multi Agent System // Somya Rai | Maria Zhang // Agents in Production 2025

Maria Zhang, CEO of Palona AI, and Somya Rai, Principal AI Engineer at EXL, discuss the architecture, scaling, memory management, and cost optimization of multi-agent systems in their respective domains of restaurants and insurance. They explore practical challenges, such as real-world bottlenecks and regulatory compliance, and share their technical stacks, including LangGraph, Ray, and NVIDIA platforms, for building robust and efficient agentic solutions.

Beyond Prompting: The Emerging Discipline of Context Engineering Reading Group

Beyond Prompting: The Emerging Discipline of Context Engineering Reading Group

This summary covers a deep dive into the paper "A Survey of Context Engineering for Large Language Models". The discussion reframes the conversation from simple prompt engineering to a more systematic approach of building information environments for LLMs. It explores the foundational components of context engineering—generation, processing, and management—and their application in advanced systems like Retrieval-Augmented Generation (RAG), memory, tool use, and multi-agent systems.

Building Multi-Player AI Systems (and why it’s SO hard)

Building Multi-Player AI Systems (and why it’s SO hard)

MeshAgent introduces a multiplayer AI paradigm, shifting from single-user systems to collaborative 'Rooms' where teams of humans and agents can work together with shared context. This talk explores the platform's architecture, developer tools, and its approach to solving real-world collaborative tasks.