Orchestration

Paperclip: Open Source Human Control Plane for AI Labor — Dotta Bippa

Paperclip: Open Source Human Control Plane for AI Labor — Dotta Bippa

Dotta, the creator of Paperclip, introduces it as an open-source orchestrator for building "zero-human companies." This talk demonstrates how to set up an organization of AI agents, leverage skills and custom instructions for reliable work, and automate business processes. Through a live demo, Dotta showcases creating a company from scratch, managing agent workflows with QA and routines, and outlines the exciting future roadmap for the platform.

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

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.

How to Pass Context in an Agentic AI Flow

How to Pass Context in an Agentic AI Flow

Grant Miller contrasts the static, single-application context of traditional OAuth with the dynamic, multi-system nature of agentic AI. He explains that agentic flows, involving orchestration, multiple agents, and LLMs, require a more sophisticated approach than simple prompt engineering. The video introduces 'context engineering' as the key strategy, which involves managing the entire system state, user context, and task history to optimize AI interactions and deliver accurate, context-aware responses.

Write Reliable Software with Temporal

Write Reliable Software with Temporal

Johann Schleier-Smith from Temporal explains Durable Execution, a paradigm for building reliable, long-running applications. He details how Temporal's model of deterministic workflows and stateful activities provides a robust alternative to traditional checkpointing and event-driven architectures, especially for complex, LLM-driven agentic systems.

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.

Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel

Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel

Learn how Vercel's open-source Workflows platform simplifies deploying durable, observable, and long-running AI agents by abstracting away the infrastructure complexities of queues, databases, and error handling.