I used to think bigger prompts meant better results. Throw everything at Claude, give it every context, and let it figure it out.
That works until it doesn't. Complex workflows break single-agent systems. I learned this building a content audit system that needed to analyze SEO, readability, brand voice, and technical performance. One agent tried to do everything and did nothing well.
The solution: multi-agent systems. Multiple specialized agents working together, each focused on what they do best.
When Single Agents Hit Their Limits
Single agents fail predictably on complex tasks:
Context overflow — Even with large context windows, cramming everything into one prompt dilutes focus. The agent tries to optimize for everything and excels at nothing.
Conflicting objectives — Ask an agent to be both creative and analytical, and you get mediocre results on both fronts.
Sequential dependencies — Complex workflows need intermediate validation. Single agents can't easily pause, validate, and course-correct mid-task.
I see this constantly in production systems. A single agent handling customer support tries to be empathetic, technical, and decisive simultaneously. It ends up being none of those things effectively.
Architecture Patterns That Work
The Specialist Pattern
Each agent owns a specific domain with clear inputs and outputs.
// SEO Analysis Agent
const seoAgent = {
role: "SEO Specialist",
input: { url: string, content: string },
output: {
score: number,
issues: string[],
recommendations: string[]
}
}
// Content Quality Agent
const qualityAgent = {
role: "Content Analyst",
input: { content: string, brandGuidelines: string },
output: {
readabilityScore: number,
brandAlignment: number,
improvements: string[]
}
}
Each agent has a single responsibility and well-defined interfaces. This makes them reliable and testable.
The Orchestrator Pattern
A coordinator agent manages the workflow and combines results.
async function auditContent(url: string) {
// Parallel execution for independent tasks
const [seoResults, qualityResults, technicalResults] = await Promise.all([
seoAgent.analyze(url),
qualityAgent.analyze(url),
technicalAgent.analyze(url)
]);
// Coordinator synthesizes results
return orchestrator.synthesize({
seo: seoResults,
quality: qualityResults,
technical: technicalResults
});
}
The orchestrator doesn't do the work—it coordinates specialists and synthesizes their outputs into actionable insights.
The Pipeline Pattern
For sequential workflows where each step depends on the previous one.
async function processLead(rawLead: string) {
// Extract structured data
const structured = await extractionAgent.process(rawLead);
// Enrich with external data
const enriched = await enrichmentAgent.process(structured);
// Score and route
const scored = await scoringAgent.process(enriched);
// Generate personalized response
return responseAgent.process(scored);
}
Each agent validates its inputs and produces clean outputs for the next stage. If any step fails, you know exactly where and why.
Coordination Strategies
Message Passing
Agents communicate through structured messages with clear schemas.
interface AgentMessage {
from: string;
to: string;
type: "request" | "response" | "notification";
payload: any;
timestamp: string;
}
This creates an audit trail and makes debugging straightforward.
Shared State
Use a central state store for data that multiple agents need.
class WorkflowState {
private state = new Map();
set(key: string, value: any, agent: string) {
this.state.set(key, { value, updatedBy: agent, timestamp: Date.now() });
}
get(key: string) {
return this.state.get(key)?.value;
}
}
Agents read and write to shared state, but ownership is clear through the audit trail.
Event-Driven Architecture
Agents publish events when they complete work. Other agents subscribe to relevant events.
// SEO agent publishes completion
eventBus.publish('seo.analysis.complete', {
url,
score: 85,
criticalIssues: ['missing meta description']
});
// Content agent subscribes to SEO events
eventBus.subscribe('seo.analysis.complete', (data) => {
if (data.score < 70) {
contentAgent.prioritizeOptimization(data.url);
}
});
This decouples agents while enabling reactive workflows.
When Multi-Agent Complexity Is Worth It
Multi-agent systems add operational complexity. They're worth it when:
Domain expertise matters — Financial analysis, legal review, and technical auditing require different knowledge bases. Specialists outperform generalists.
Parallel processing improves speed — Independent tasks can run simultaneously instead of sequentially.
Validation is critical — Each agent validates inputs and outputs, creating multiple checkpoints for quality.
Scaling requires specialization — Adding new capabilities means adding new agents, not modifying existing ones.
I built a marketing performance agent that pulls from GA4, Google Ads, Search Console, and social media APIs. A single agent couldn't effectively analyze all these data sources. Specialized agents for each platform produce better insights faster.
Implementation with Claude and MCP
Claude's function calling makes building multi-agent systems straightforward.
const agents = {
seo: new ClaudeAgent({
model: "claude-3-5-sonnet-20241022",
systemPrompt: "You are an SEO specialist...",
tools: [seoAnalysisTool, keywordTool]
}),
content: new ClaudeAgent({
model: "claude-3-5-sonnet-20241022",
systemPrompt: "You are a content strategist...",
tools: [readabilityTool, brandAnalysisTool]
})
};
MCP servers can provide specialized tools for each agent, keeping concerns separated.
Common Pitfalls to Avoid
Over-engineering — Start with a single agent. Split when you hit clear limitations, not preemptively.
Unclear ownership — Every piece of data should have a clear owner. Shared ownership leads to inconsistent updates.
Chatty interfaces — Agents shouldn't negotiate or have conversations. They should exchange structured data.
No fallback strategies — When one agent fails, the system should degrade gracefully, not crash entirely.
Making It Production-Ready
Multi-agent systems need robust monitoring and error handling.
class AgentOrchestrator {
async execute(workflow: string, input: any) {
const trace = new WorkflowTrace(workflow);
try {
for (const step of this.getSteps(workflow)) {
trace.startStep(step.name);
const result = await step.agent.execute(input);
trace.completeStep(step.name, result);
input = result;
}
return input;
} catch (error) {
trace.fail(error);
await this.handleFailure(workflow, trace, error);
throw error;
}
}
}
Track which agent handled each step, what inputs they received, and what outputs they produced. When things break—and they will—you'll know exactly where.
The Bottom Line
Multi-agent systems aren't about using more AI. They're about using AI more effectively.
Single agents excel at focused tasks with clear objectives. Multi-agent systems excel at complex workflows that require different types of expertise.
The key is knowing when to make the jump. Start simple, measure results, and add complexity only when it solves real problems.
Ready to build your first multi-agent system? Let's discuss your specific use case and design an architecture that actually works.