Design multi-agent workflows you can trust
Summary
A small team releases 'guilds', an open-source framework for designing trustworthy multi-agent AI workflows, built around principles of bounded autonomy, observability, and shareable workflow recipes.
Similar Articles
Is anyone actually orchestrating multi-agent workflows well, or are we all duct-taping?
A reflective article questioning whether anyone has successfully implemented multi-agent AI workflow orchestration without resorting to makeshift solutions.
AI Agent that builds deterministic workflows
A developer shares an experiment with an AI agent-based automation platform that builds and manages deterministic workflows, seeking feedback from the community.
I rebuilt my private "AI dev team" — which was secretly just a hardcoded workflow — as a substrate where orchestration emerges from instructions. Here's what I learned (and where it deadlocks).
The author rebuilt their private AI dev team as an open-sourced substrate with addressable agents, reliable messaging, expertise discovery, memory, and isolated runtimes, allowing team behavior to emerge from natural-language instructions. They share insights on coordination challenges such as deadlocks and self-healing, and question how agent teams can collaborate using NL instructions.
Workspace agents
This article introduces OpenAI's 'Workspace Agents' in ChatGPT, designed to handle repeatable, structured workflows rather than one-off tasks. It explains the core concepts, anatomy, and best practices for using and building these agents for consistent business processes.
Giving every engineer an AI agent without a shared workflow just automates disagreement
The article discusses the problem of inconsistent AI agent workflows in engineering teams and introduces OmniNode, a tool that standardizes agent work through explicit criteria and verification to enhance system reliability.