Multi-agent systems are sequential pipelines that look like distributed systems. A researcher...
Multi-agent systems are sequential pipelines that look like distributed systems.
A researcher gathers findings, a writer drafts, a reviewer checks.
Each agent makes API calls — to Claude, to OpenAI, to whatever LLM is doing the work.
Each call can fail mid-flight. And when one fails, you get a choice: re-run the whole pipeline from the top and burn tokens you already paid for, or wire up checkpointing yourself.
The example-multi-agent-orchestration-ts repo shows the third option.
In the previous two posts I built a minimal Claude agent (Module 1) and then gave it multiple tools...
When One Agent Isn't Enough The previous eight articles built single-agent systems: one...
Error compounding, stale state, and context rot break multi-agent LLM pipelines. Learn the failure modes and design patterns that…
Multi-agent systems fail when shared collaboration state disappears. Build a traceable collaboration plane for claims, findings,…
Picture a four-agent pipeline left running over a long weekend. Two of the agents start calling each...
Most multi-agent demos look simple. An agent receives a request, delegates a few tasks, calls some...
