Claude’s Research feature demonstrates the power of multi-agent collaboration in complex problem-solving. This chart shows the most popular use cases where multiple agents work together to decompose challenging research tasks. The lessons Anthropic shared from this implementation provide timely insights I’m planning to apply in my own multi-agent system development, that I’d overlooked before:
Test your subagents and watch them work step-by-step. Are they using the incorrect tools, have you specified an output format, and clear task boundaries? Without detailed task descriptions, agents might duplicate work, leave gaps, or fail to find necessary information.
Agents struggle to judge appropriate effort for different tasks so help them allocate resources efficiently by specifying breadth and depth to scale effort to query complexity.
A single LLM call with a single prompt outputting scores from 0.0-1.0 and a pass-fail grade was the most effective LLM-as-judge evaluation across a rubric: factual accuracy, completeness, source quality, tool efficiency, among others.
Build in a way to resume when errors occur because restarting from the beginning is expensive and frustrating.
For memory management due to limited context windows, implement artifact systems where specialized agents can create outputs that persist independently rather than requiring subagents to communicate everything through the lead agent. Make subagents call tools to store their work in external systems, then pass lightweight references back to the coordinator.
I still feel a bit stuck on what to do when subagents disagree but I suppose that’s for the lead agent to remediate. 😅
This post was originally on LinkedIn.