Multi-Agent Systems
Orchestrating multiple AI agents: coordination, state, failures, and applications
Multi-Agent Systems
Specialist vs Generalist Agents in Multi-Agent Systems
One capable generalist agent or a team of narrow specialists? The specialist vs generalist agents decision shapes your entire multi-agent architecture. The right choice depends on your task's structure, not the agent count.
·10 min read
Multi-Agent Systems
Blackboard Architecture for AI Agents
Blackboard architecture is one of the oldest multi-agent coordination patterns — and one of the most underused in LLM systems. When agents need to collaborate without tight coupling, a shared workspace outperforms direct message passing.
·12 min read
Multi-Agent Systems
Debate Between Agents to Improve Answers
When do multi-agent systems actually produce better answers through debate? The research is more specific than most tutorials admit — and the most important parameter isn't which agents you use, it's how many debate rounds you run.
·10 min read
Multi-Agent Systems
Handoffs Between Specialized Agents
Picture this: you're debugging a multi-agent pipeline where the final output is wrong, and you can't tell whether the error came from Agent A or Agent B, because the handoff between them swallowed all the context. Here's how to build handoffs that preserve what matters.
·11 min read
Multi-Agent Systems
The Router Pattern for Multi-Agent Systems
A 2025 analysis of production multi-agent failures found that misrouting — sending a task to the wrong specialist — was responsible for more quality failures than individual agent errors. The router is the most critical agent in your system.
·11 min read