5-Minute Guide to AI Agent Orchestration
Published: March 5, 2026 • Reading time: 5 minutes
You've built an AI agent. Now you need three more. And they need to work together without stepping on each other's toes.
Welcome to agent orchestration hell.
One agent reads emails. Another schedules meetings. A third generates reports. They all access the same calendar API, but they don't know about each other. Result: double-booked meetings, duplicate emails, and quota violations.
This guide shows you how to coordinate multiple agents using the Governor + Orchestrator pattern in 5 minutes. You'll build a working multi-agent data pipeline with role-based access control.
The Problem: Multi-Agent Chaos
When you have multiple autonomous agents, coordination becomes critical:
- Resource conflicts: Two agents try to write to the same database simultaneously
- Quota violations: Agents don't know about each other's API usage
- Permission gaps: Agent A can access data that Agent B shouldn't see
- Race conditions: Agent output depends on unpredictable execution order
Traditional solutions (locks, queues, manual coordination) add complexity and kill autonomy. You need orchestration that scales.
The Pattern: Governor + Orchestrator
We covered why agents need governors in our previous article. Now let's build a real multi-agent system.
Architecture:
- Governor: Policy engine that enforces rules across all agents
- Orchestrator: Runtime that routes agent actions through the Governor
- Agents: Autonomous workers that propose actions
The key insight: agents don't coordinate directly—they coordinate through the Governor.
Tutorial: Multi-Agent Data Pipeline
Let's build a data pipeline with three agents:
1. Fetcher Agent: Downloads data from external APIs
2. Processor Agent: Transforms and validates data
3. Writer Agent: Saves results to database
Each agent has different permissions. The Governor enforces them.
Step 1: Define RBAC Policies
// policies/rbac-policy.js
const rolePermissions = {
fetcher: ['read:api', 'write:temp_storage'],
processor: ['read:temp_storage', 'write:temp_storage'],
writer: ['read:temp_storage', 'write:database']
};
const rbacPolicy = {
check: (action, context) => {
const agentRole = context.agent.role;
const allowedActions = rolePermissions[agentRole] || [];
const actionKey = ${action.permission}:${action.resource};
if (!allowedActions.includes(actionKey)) {
return {
blocked: true,
reason: Role '${agentRole}' not permitted for '${actionKey}'
};
}
return { blocked: false };
}
};
module.exports = rbacPolicy;
Step 2: Add Resource Management
Prevent quota violations with a rate limit policy:
// policies/rate-limit-policy.js
const quotaTracker = new Map(); // Track API usage per agent
const rateLimitPolicy = {
check: (action, context) => {
if (action.type !== 'api_call') return { blocked: false };
const agentId = context.agent.id;
const usage = quotaTracker.get(agentId) || { calls: 0, resetAt: Date.now() + 60000 };
// Reset quota every minute
if (Date.now() > usage.resetAt) {
usage.calls = 0;
usage.resetAt = Date.now() + 60000;
}
// Limit: 10 calls/minute per agent
if (usage.calls >= 10) {
return {
blocked: true,
reason: 'Rate limit exceeded (10 calls/min)'
};
}
usage.calls++;
quotaTracker.set(agentId, usage);
return { blocked: false };
}
};
module.exports = rateLimitPolicy;
Step 3: Build the Orchestrator
// orchestrator.js
const Governor = require('./governor');
const rbacPolicy = require('./policies/rbac-policy');
const rateLimitPolicy = require('./policies/rate-limit-policy');
class PipelineOrchestrator {
constructor() {
this.governor = new Governor([rbacPolicy, rateLimitPolicy]);
this.agents = new Map();
}
registerAgent(agent) {
this.agents.set(agent.id, agent);
}
async execute(agentId, action) {
const agent = this.agents.get(agentId);
if (!agent) throw new Error(Agent '${agentId}' not found);
// Governor evaluates action with agent context
const result = this.governor.evaluate(action, { agent });
if (!result.approved) {
console.log([Governor] Blocked: ${result.reason});
return { success: false, reason: result.reason };
}
// Execute approved action
console.log([Orchestrator] Executing: ${action.type});
const output = await agent.run(result.action);
return { success: true, output };
}
}
module.exports = PipelineOrchestrator;
Step 4: Define the Agents
// agents/fetcher-agent.js
class FetcherAgent {
constructor() {
this.id = 'fetcher-1';
this.role = 'fetcher';
}
async run(action) {
if (action.type === 'fetch_data') {
// Simulate API call
const data = await fetch(action.url);
return { status: 'fetched', records: data.length };
}
}
}
// agents/processor-agent.js
class ProcessorAgent {
constructor() {
this.id = 'processor-1';
this.role = 'processor';
}
async run(action) {
if (action.type === 'transform_data') {
// Simulate data transformation
return { status: 'transformed', records: action.data.length };
}
}
}
// agents/writer-agent.js
class WriterAgent {
constructor() {
this.id = 'writer-1';
this.role = 'writer';
}
async run(action) {
if (action.type === 'write_database') {
// Simulate database write
return { status: 'written', records: action.data.length };
}
}
}
Step 5: Run the Pipeline
// main.js
const PipelineOrchestrator = require('./orchestrator');
const FetcherAgent = require('./agents/fetcher-agent');
const ProcessorAgent = require('./agents/processor-agent');
const WriterAgent = require('./agents/writer-agent');
const orchestrator = new PipelineOrchestrator();
// Register agents
const fetcher = new FetcherAgent();
const processor = new ProcessorAgent();
const writer = new WriterAgent();
orchestrator.registerAgent(fetcher);
orchestrator.registerAgent(processor);
orchestrator.registerAgent(writer);
// Execute pipeline
async function runPipeline() {
// Step 1: Fetcher downloads data
const fetchResult = await orchestrator.execute('fetcher-1', {
type: 'fetch_data',
permission: 'read',
resource: 'api',
url: 'https://api.example.com/data'
});
if (!fetchResult.success) {
console.error('Pipeline failed at fetch:', fetchResult.reason);
return;
}
// Step 2: Processor transforms data
const processResult = await orchestrator.execute('processor-1', {
type: 'transform_data',
permission: 'write',
resource: 'temp_storage',
data: fetchResult.output
});
// Step 3: Writer saves to database
const writeResult = await orchestrator.execute('writer-1', {
type: 'write_database',
permission: 'write',
resource: 'database',
data: processResult.output
});
console.log('Pipeline complete:', writeResult);
}
runPipeline();
What You Get
RBAC enforcement: Each agent can only access resources for its role. The Writer agent can't call external APIs. The Fetcher can't write to the database.
Rate limiting: Agents share a quota. No single agent can burn through your API budget.
Audit trail: Every action goes through the Governor. You see exactly what was approved or blocked.
Safe coordination: Agents don't need to know about each other. The Governor handles conflicts.
Try It: 5 violations blocked
Run the code above, then try these violations:
// Violation 1: Fetcher tries to write to database (RBAC blocked)
orchestrator.execute('fetcher-1', {
type: 'write_database',
permission: 'write',
resource: 'database'
});
// Violation 2: Agent exceeds rate limit (quota blocked)
for (let i = 0; i < 15; i++) {
orchestrator.execute('fetcher-1', { type: 'api_call' });
}
The Governor blocks both. Your system stays safe.
Production-Ready Orchestration
Want this pattern production-ready?
Dracanus provides:
- Pre-built RBAC, rate limiting, and cost policies
- Real-time monitoring dashboard
- Audit logs for compliance
- Policy updates without redeployment
👉 Try the demo: dracanus.app
Build multi-agent systems that coordinate safely. 5-minute setup, no infrastructure required.
Next: Read Why Your AI Agents Need a Governor for the architectural foundation behind this pattern.
About Dracanus: We build governance infrastructure for autonomous AI agents. Questions? Find us at dracanus.app