Applicability
When to Use
✓When tasks require multiple domains of expertise
✓When agents need to collaborate on complex problems
✓When you want to scale agent capabilities modularly
Overview
How It Works
The Multi-Agent pattern assigns different domains to specialized agents, each with access to specific MCP servers. A coordinator agent breaks down complex tasks, delegates subtasks to specialists, and combines their results.
For example, a DevOps task might involve a code agent (GitHub MCP Server), an infrastructure agent (Terraform MCP Server), and a monitoring agent (Datadog MCP Server). Each agent excels at its domain, and the coordinator ensures they work together coherently.
Implementation
Code Example
typescript
const agents = {
code: { servers: ["github", "sourcegraph"], expertise: "code changes and reviews" },
infra: { servers: ["terraform", "aws"], expertise: "infrastructure management" },
monitor: { servers: ["datadog", "pagerduty"], expertise: "monitoring and incidents" }
};
async function coordinateTask(task) {
// Break down the task
const subtasks = analyzeTask(task);
// Delegate to specialists
const results = await Promise.all(
subtasks.map(async (subtask) => {
const agent = selectAgent(subtask, agents);
return { agent: agent.name, result: await agent.execute(subtask) };
})
);
// Combine results
return synthesizeResults(results);
}
function selectAgent(subtask, agents) {
if (subtask.involves("code")) return agents.code;
if (subtask.involves("infrastructure")) return agents.infra;
if (subtask.involves("monitoring")) return agents.monitor;
}Quick Info
Categoryai-agent
ComplexityHard