Multi-Agent Observability FAQs

Question 1

Does this require a specific SDK?

Accepted Answer

While these patterns are universal, full implementation is specifically designed to work with the Claude Agent SDK using custom MCP tools and UI components.

Question 2

What is Multi-Agent Observability?

Accepted Answer

Multi-Agent Observability is the practice of monitoring, logging, and measuring the performance and behavior of multiple AI agents working together in a system to ensure reliability and transparency.

Question 3

How does this skill help track AI costs?

Accepted Answer

The skill provides specific implementation patterns for calculating input and output token usage per agent, allowing you to monitor spend by model type and aggregate total costs for a specific task.

Question 4

Can I use this for real-time debugging?

Accepted Answer

Yes, it includes designs for real-time event streams and log viewers that allow you to see exactly which tools agents are calling and what results they are producing as it happens.

Question 5

What are the most important metrics to observe in agent fleets?

Accepted Answer

Essential metrics include agent status (idle, executing, error), context window usage, tool call frequency, execution duration, and success rates for produced assets.

Multi-Agent Observability

About

Key Features

Use Cases

Multi-Agent Observability

About

Key Features

Use Cases