Building a Production-Ready Multi-Agent Investment Committee with AgentField

TL;DR This tutorial walks through building Argus, a multi-agent system that performs automated stock research. Using AgentField, agents run as modular microservices with typed skills and reasoners. The architecture enables parallel analysis, structured workflows, and full observability for production-ready AI systems. Introduction Many early Agentic applications start with high-level orchestrators like LangChain or CrewAI. For simple use cases, these frameworks work well and are often the fastest way to prototype an idea. However, as the complexity of the task grows, especially when moving from a notebook to a production service, this pattern begins to break down. Traditional agent frameworks often focus on "agentic reasoning" but neglect the "production engineering." In a real-world system, you can't just have one model or orchestrator responsible for multiple stages of a workflow in a sequential, opaque loop. Failures are harder to trace because the entire workflow is coupled to a si