Multi agent systems

Apr 08, 2026

From Chaos to Choreography: Multi-Agent Orchestration Patterns That Actually Work — Sandipan Bhaumik

Sandipan Bhaumik from Databricks explains that scaling from one to many AI agents is a distributed systems problem, not an AI one. He details common architectural anti-patterns like shared mutable state that cause race conditions and silent failures. The talk provides a practical framework based on distributed systems engineering, covering crucial patterns like choreography vs. orchestration, immutable state management with versioning, data contracts, and failure recovery using circuit breakers and compensation (Saga) patterns. Bhaumik illustrates how to build a robust, production-grade multi-agent architecture using tools like Databricks, LangGraph, and MLflow.

Apr 01, 2026

Large-scale agentic quant research with Weights & Biases

Explore how Weights & Biases (W&B) enhances reliability, reproducibility, and explainability in large-scale, agent-driven quantitative research. This video demonstrates two core applications: debugging multi-agent alpha research pipelines with W&B Weave to identify root causes and iterate on forecasts, and automating strategy optimization using W&B Models to tune agent weights and gain insights from performance convergence and parallel coordinate plots.

Mar 26, 2026

A Common-Sense Guide to AI Engineering • Jay Wengrow & Kris Jenkins • GOTO 2026

Jay Wengrow, author of “A Common-Sense Guide to AI Engineering,” breaks down how AI agents work, describing the 'clever hack' of intercepting LLM output to trigger functions. The discussion covers multi-agent architectures for complex tasks, implementing guardrails with regex and judge LLMs, and a pragmatic take on when to use frameworks versus building from scratch. Wengrow emphasizes understanding fundamentals over specific tools to create robust, production-ready AI applications.

Mar 12, 2026

What Are Hierarchical AI Agents? Solving Context & Task Challenges

Explores the challenges of single AI agents, such as context dilution and tool overload, and introduces hierarchical AI agents as a solution. This summary details the structure, benefits, and limitations of multi-agent systems for more scalable and efficient AI workflows.

Mar 02, 2026

A2A vs MCP: AI Agent Communication Explained

Discover how A2A (Agent2Agent) and MCP (Model Context Protocol) solve critical challenges in AI agent ecosystems. A2A enables seamless communication and collaboration between diverse AI agents, while MCP standardizes an agent's access to external tools and data, fostering robust and interoperable AI workflows.

Feb 26, 2026

GeoMind: A Multi-Agent Framework for Geospatial Decision Support

GeoMind is a multi-agent framework designed to empower non-technical users, such as disaster responders, to perform complex geospatial analysis using natural language. It bridges the gap between Large Language Models and advanced GIS workflows by employing a team of specialized AI agents that can query, join, and analyze multi-layered vector and raster data to provide timely, actionable insights during emergencies.