Plan a multi-agent orchestration framework for scalable AI

Real-world deployments of agentic AI have so far been limited in scope, despite strong interest in using the technology to automate many of the business processes now handled by people. One reason for the slow deployment of agents is the challenge of multi-agent orchestration: the ability of AI agents to communicate with each other and coordinate their activities across enterprise applications, workflows and even corporate firewalls. There is growing recognition that developing a framework for multi-agent orchestration is essential for deploying agents on a large scale across the entire organization. In this episode, we explore the main elements of a multi-agent framework, the problems it is meant to address, who is responsible for developing it and where to find ready-made frameworks and tools. Featuring: Peter Hesse, Partner, 10Pearls     In today's episode, we'll also cover: Why the tendency of agents to work in harmony can make them less resilient when their scale expands. How a framework can support AI transparency and traceability. Using "policy as code" to enforce AI consistency and trust. References:  AI agent frameworks: A guide to evaluating agentic platforms Real-world agentic AI examples and use cases 10Pearls blog post on building enterprise AI agent frameworks To learn more about enterprise applications, check out Search ERP.