Trigger.dev + Multi-Agent Orchestration: Our Stack Explained

Why Multi-Agent Orchestration Matters for Answer Engine Optimization

You're losing visibility in AI search. Your competitors are getting cited in ChatGPT, Perplexity, and Gemini, while your brand stays invisible. The problem isn't your content quality. It's that you're still thinking in single-channel terms.

Answer engine optimization demands a different approach. Instead of one SEO strategy, you need multiple specialized agents working in parallel: one analyzing competitor citations, another optimizing your content for specific AI engines, a third monitoring your real-time visibility across ChatGPT, Perplexity, Gemini, Claude, Grok, and Google. Running these tasks manually, or worse, sequentially, means you're always behind.

This is where multi-agent orchestration enters. By using Trigger.dev to coordinate specialized AI agents, you can automate the entire AEO workflow at scale. Each agent handles a specific task. Trigger.dev ensures they run in the right order, share data seamlessly, and scale without breaking.

SearchFit uses this exact stack to help B2B SaaS companies, marketing agencies, and e-commerce brands track their AI visibility and generate content engineered to rank in answer engines. This guide walks you through how we built it, and how you can implement the same pattern for your own multi-agent content systems.

Prerequisites: What You Need Before Starting

Before you begin building your multi-agent orchestration stack, ensure you have the following in place:

Technical Requirements:

• Node.js 18+ installed on your development machine
• A TypeScript development environment (VS Code or similar)
• Git for version control
• Access to the Trigger.dev platform (free tier available)
• API keys for your AI providers (OpenAI, Anthropic, or similar)
• A basic understanding of async/await patterns in JavaScript

Knowledge Prerequisites:

• Familiarity with REST APIs and HTTP requests
• Understanding of task queues and background job concepts
• Basic knowledge of how LLMs and agents work
• Experience with environment variables and secrets management
• Comfort reading TypeScript code

Infrastructure Needs:

• A database for storing agent outputs (PostgreSQL, MongoDB, or similar)
• A webhook endpoint to receive real-time updates from Trigger.dev
• Storage for large content files (S3, Google Cloud Storage, or local filesystem)
• Monitoring tools to track job success and failure rates

Account Setup:

• Create a free Trigger.dev account at the official Trigger.dev GitHub repository
• Set up your project workspace
• Generate API keys for authentication
• Configure your environment variables securely

Understanding the Multi-Agent Architecture

Before diving into implementation, you need to understand the architecture we're building. Multi-agent orchestration isn't just running agents in parallel, it's coordinating specialized workers that depend on each other's outputs.

In our SearchFit stack, we have five core agents:

The Citation Agent crawls AI search engines and identifies which domains are being cited in answers. It feeds this data to the next agent.

The Content Analysis Agent examines your competitors' cited content and extracts the patterns that make content rankable in AI search. It looks for structure, keyword density, citation signals, and answer format.

The Content Generation Agent takes those patterns and creates new content optimized for AI visibility. It doesn't just write, it engineers content specifically for how ChatGPT, Perplexity, and Gemini consume and cite sources.

The Visibility Tracking Agent continuously monitors your brand's appearance across AI search engines. It tracks whether your content is being cited, how often, and in what context.

The Reporting Agent aggregates all this data and generates insights. It tells you: "Your brand went from 0% AI visibility to 12% this month. Here's why. Here's what to do next."

Each agent is specialized. Each has a single responsibility. But they work together, the Citation Agent's output becomes the Content Analysis Agent's input, which feeds the Content Generation Agent, and so on.

Trigger.dev's job is to orchestrate this flow. It ensures tasks run in the right sequence, handles failures gracefully, retries when needed, and scales without manual intervention.

Step 1: Set Up Your Trigger.dev Project and Environment

Start by initializing a new Trigger.dev project. This is your orchestration engine.

npm create trigger.dev@latest my-aeo-agents
cd my-aeo-agents

This command scaffolds a TypeScript project with Trigger.dev pre-configured. You'll see a folder structure with src/ for your code, trigger/ for job definitions, and configuration files.

Next, install the core dependencies:

npm install @trigger.dev/sdk openai anthropic dotenv axios

Now create your environment file. In the root directory, create .env.local:

TRIGGER_API_KEY=your_trigger_dev_api_key
TRIGGER_API_URL=https://api.trigger.dev
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
DATABASE_URL=your_database_connection_string
SEARCHFIT_API_KEY=your_searchfit_api_key

Replace each placeholder with your actual credentials. Store these securely, never commit them to version control.

Next, authenticate with Trigger.dev:

npx trigger.dev login

This opens a browser window where you authorize the CLI to access your Trigger.dev account. Once authenticated, you can deploy jobs directly from your terminal.

Create your first job file. In src/jobs/, create citation-agent.ts:

import { client } from "@trigger.dev/sdk/v3";

export const citationAgent = client.defineJob({
id: "citation-agent",
name: "Citation Tracking Agent",
version: "1.0.0",
trigger: client.cron({
cron: "0 */6 * * *", // Every 6 hours
}),
run: async (payload, io, ctx) => {
// Citation tracking logic here
return { citations: [] };
},
});

This defines a scheduled job that runs every 6 hours. The run function contains your agent logic. We'll populate it in the next steps.

Deploy your job:

npx trigger.dev deploy

Trigger.dev compiles your code, uploads it to their servers, and makes it available for execution. You'll see a URL where you can monitor your job in real-time.

Step 2: Build the Citation Agent

The Citation Agent is your first specialized worker. Its job: find which domains are being cited in AI search results.

Update your citation-agent.ts file:

import { client } from "@trigger.dev/sdk/v3";
import axios from "axios";

interface CitationResult {
domain: string;
engine: string;
citationCount: number;
lastSeen: Date;
context: string;
}

export const citationAgent = client.defineJob({
id: "citation-agent",
name: "Citation Tracking Agent",
version: "1.0.0",
trigger: client.cron({
cron: "0 */6 * * *",
}),
run: async (payload, io, ctx) => {
const engines = ["chatgpt", "perplexity", "gemini", "claude", "grok"];
const keywords = [
"SaaS analytics",
"SEO tools",
"content optimization",
];
const citations: CitationResult[] = [];

for (const engine of engines) {
for (const keyword of keywords) {
try {
const response = await io.runTask(
`fetch-${engine}-${keyword}`,
async () => {
// Call SearchFit API to get citations
const result = await axios.post(
"https://api.searchfit.ai/track",
{
engine,
keyword,
limit: 10,
},
{
headers: {
Authorization: `Bearer ${process.env.SEARCHFIT_API_KEY}`,
},
}
);
return result.data;
}
);

citations.push(...response.citations);
} catch (error) {
await io.logger.error(`Failed to fetch ${engine} citations`, error);
}
}
}

// Pass citations to the next agent
return { citations, timestamp: new Date() };
},
});

This agent:

1. Loops through five AI engines
2. Queries SearchFit's tracking API for each keyword
3. Collects all citations
4. Returns structured data for the next agent

The io.runTask() wrapper is crucial, it tells Trigger.dev to treat this as a discrete, retriable unit of work. If the API call fails, Trigger.dev retries automatically.

Deploy this updated version:

npx trigger.dev deploy

You can now monitor your Citation Agent in the Trigger.dev dashboard. Watch real-time logs, see execution times, and track failure rates.

Step 3: Build the Content Analysis Agent

Once citations are collected, the Content Analysis Agent examines what makes those cited sources rank. It extracts patterns, structure, length, keyword placement, answer format, that predict AI visibility.

Create src/jobs/content-analysis-agent.ts:

import { client } from "@trigger.dev/sdk/v3";
import axios from "axios";
import OpenAI from "openai";

interface ContentPattern {
domain: string;
wordCount: number;
hasStructuredData: boolean;
headingStructure: string[];
citationFrequency: number;
answerFormat: string;
}

const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
});

export const contentAnalysisAgent = client.defineJob({
id: "content-analysis-agent",
name: "Content Analysis Agent",
version: "1.0.0",
trigger: client.on({
event: "citation-agent.completed",
}),
run: async (payload, io, ctx) => {
const { citations } = payload;
const patterns: ContentPattern[] = [];

for (const citation of citations) {
try {
const contentAnalysis = await io.runTask(
`analyze-${citation.domain}`,
async () => {
// Fetch the actual content
const content = await axios.get(
`https://api.searchfit.ai/content?domain=${citation.domain}`
);

// Use GPT to analyze patterns
const analysis = await openai.chat.completions.create({
model: "gpt-4",
messages: [
{
role: "system",
content:
"You are an expert in answer engine optimization. Analyze this content and explain why it ranks in AI search results.",
},
{
role: "user",
content: content.data.body,
},
],
temperature: 0.3,
});

return {
domain: citation.domain,
wordCount: content.data.wordCount,
hasStructuredData: content.data.hasSchema,
headingStructure: content.data.headings,
citationFrequency: citation.citationCount,
answerFormat: analysis.choices[0].message.content,
};
}
);

patterns.push(contentAnalysis);
} catch (error) {
await io.logger.warn(`Failed to analyze ${citation.domain}`, error);
}
}

return { patterns, analyzedAt: new Date() };
},
});

Notice the trigger: client.on({ event: "citation-agent.completed" }). This means the Content Analysis Agent automatically runs when the Citation Agent finishes. Trigger.dev handles this orchestration for you.

The agent:

1. Receives citation data from the previous job
2. Fetches the actual content from each cited domain
3. Uses GPT-4 to analyze why that content ranks
4. Extracts patterns (word count, structure, format)
5. Passes this intelligence to the next agent

Deploy:

npx trigger.dev deploy

Now you have a two-agent pipeline. The Citation Agent finds who's ranking. The Content Analysis Agent explains why.

Step 4: Build the Content Generation Agent

With patterns extracted, the Content Generation Agent creates new content optimized for those patterns. It's not just writing, it's engineering content specifically for AI search.

Create src/jobs/content-generation-agent.ts:

import { client } from "@trigger.dev/sdk/v3";
import Anthropic from "@anthropic-ai/sdk";

interface GeneratedContent {
title: string;
body: string;
structure: string[];
estimatedAIVisibility: number;
targetEngines: string[];
}

const anthropic = new Anthropic({
apiKey: process.env.ANTHROPIC_API_KEY,
});

export const contentGenerationAgent = client.defineJob({
id: "content-generation-agent",
name: "Content Generation Agent",
version: "1.0.0",
trigger: client.on({
event: "content-analysis-agent.completed",
}),
run: async (payload, io, ctx) => {
const { patterns } = payload;
const generatedContent: GeneratedContent[] = [];

// Extract common patterns from all analyzed content
const avgWordCount =
patterns.reduce((sum: number, p: ContentPattern) => sum + p.wordCount, 0) / patterns.length;
const commonHeadingStructure = patterns[0].headingStructure;

for (const topic of [
"answer engine optimization",
"AI visibility tracking",
"generative engine optimization",
]) {
const content = await io.runTask(
`generate-${topic.replace(/\s+/g, "-")}`,
async () => {
const message = await anthropic.messages.create({
model: "claude-3-5-sonnet-20241022",
max_tokens: 2000,
messages: [
{
role: "user",
content: `Create a comprehensive article about "${topic}" optimized for AI search engines.

Based on analysis of top-ranking content:
- Target word count: ${avgWordCount} words
- Use this heading structure: ${commonHeadingStructure.join(", ")}
- Include practical examples and specific metrics
- Write for technical decision-makers
- Optimize for ChatGPT, Perplexity, Gemini, and Claude citations

Return the article with clear sections and actionable insights.`,
},
],
});

return {
title: `${topic.charAt(0).toUpperCase() + topic.slice(1)}: Complete Guide`,
body:
message.content[0].type === "text" ? message.content[0].text : "",
structure: commonHeadingStructure,
estimatedAIVisibility: 0.35, // Will be updated by visibility agent
targetEngines: [
"chatgpt",
"perplexity",
"gemini",
"claude",
"grok",
],
};
}
);

generatedContent.push(content);
}

return { generatedContent, createdAt: new Date() };
},
});

This agent:

1. Receives content patterns from the analysis agent
2. Extracts common characteristics (word count, structure)
3. Generates new content engineered to match those patterns
4. Uses Claude (via Anthropic) for high-quality writing
5. Tags content for specific AI engines

The key insight: you're not guessing what AI engines want. You're learning from what already ranks and replicating those patterns at scale.

Deploy:

npx trigger.dev deploy

Step 5: Build the Visibility Tracking Agent

After content is generated, the Visibility Tracking Agent monitors whether it actually ranks in AI search. This closes the feedback loop.

Create src/jobs/visibility-tracking-agent.ts:

import { client } from "@trigger.dev/sdk/v3";
import axios from "axios";

interface VisibilityMetric {
contentId: string;
engine: string;
visible: boolean;
citationCount: number;
answerPosition: number | null;
confidence: number;
}

export const visibilityTrackingAgent = client.defineJob({
id: "visibility-tracking-agent",
name: "Visibility Tracking Agent",
version: "1.0.0",
trigger: client.on({
event: "content-generation-agent.completed",
}),
run: async (payload, io, ctx) => {
const { generatedContent } = payload;
const metrics: VisibilityMetric[] = [];

for (const content of generatedContent) {
for (const engine of content.targetEngines) {
const visibility = await io.runTask(
`track-${content.title}-${engine}`,
async () => {
// Use SearchFit's AI Tracking to monitor visibility
const response = await axios.post(
"https://api.searchfit.ai/ai-tracking/monitor",
{
contentId: content.title,
engine,
keywords: [content.title],
},
{
headers: {
Authorization: `Bearer ${process.env.SEARCHFIT_API_KEY}`,
},
}
);

return {
contentId: content.title,
engine,
visible: response.data.visible,
citationCount: response.data.citations || 0,
answerPosition: response.data.position || null,
confidence: response.data.confidence || 0,
};
}
);

metrics.push(visibility);
}
}

return { metrics, trackedAt: new Date() };
},
});

This agent:

1. Takes newly generated content
2. Monitors each piece across all five AI engines
3. Tracks whether it's being cited
4. Records position in answers (if visible)
5. Stores confidence scores

The data feeds your reporting dashboard. You'll see exactly which content is working and which needs iteration.

Step 6: Build the Reporting Agent and Close the Loop

The final agent aggregates everything and generates actionable insights.

Create src/jobs/reporting-agent.ts:

import { client } from "@trigger.dev/sdk/v3";
import axios from "axios";

interface Report {
summary: string;
totalVisibility: number;
visibilityByEngine: Record<string, number>;
topPerformingContent: string[];
recommendations: string[];
generatedAt: Date;
}

export const reportingAgent = client.defineJob({
id: "reporting-agent",
name: "Reporting Agent",
version: "1.0.0",
trigger: client.on({
event: "visibility-tracking-agent.completed",
}),
run: async (payload, io, ctx) => {
const { metrics } = payload;

const report = await io.runTask("generate-report", async () => {
const visibleContent = metrics.filter(
(m: VisibilityMetric) => m.visible
);
const totalVisibility = (
(visibleContent.length / metrics.length) *
100
).toFixed(2);

const visibilityByEngine = metrics.reduce(
(acc: Record<string, number>, m: VisibilityMetric) => {
if (!acc[m.engine]) acc[m.engine] = 0;
if (m.visible) acc[m.engine]++;
return acc;
},
{}
);

const topPerforming = visibleContent
.sort((a: VisibilityMetric, b: VisibilityMetric) => b.citationCount - a.citationCount)
.slice(0, 3)
.map((m: VisibilityMetric) => m.contentId);

const recommendations = [
totalVisibility > 50
? "Your AEO strategy is working. Scale content production."
: "Visibility is low. Review content patterns and optimize structure.",
Object.entries(visibilityByEngine).length === 5
? "You're ranking across all five major AI engines. Maintain this breadth."
: "You're missing some engines. Adjust content for broader AI coverage.",
];

return {
summary: `Your brand has ${totalVisibility}% visibility across AI search engines.`,
totalVisibility: parseFloat(totalVisibility as string),
visibilityByEngine,
topPerformingContent: topPerforming,
recommendations,
generatedAt: new Date(),
};
});

// Store report in database
await io.runTask("store-report", async () => {
await axios.post(
"https://api.searchfit.ai/analytics/reports",
report,
{
headers: {
Authorization: `Bearer ${process.env.SEARCHFIT_API_KEY}`,
},
}
);
});

return report;
},
});

This agent:

1. Aggregates metrics from all visibility tracking
2. Calculates overall AI visibility percentage
3. Breaks down performance by engine
4. Identifies top-performing content
5. Generates recommendations
6. Stores the report for your dashboard

Deploy:

npx trigger.dev deploy

Now you have a complete five-agent pipeline. Each agent specializes. Trigger.dev orchestrates. Data flows automatically from one agent to the next.

Step 7: Connect Agents with Event-Driven Triggers

You've built five agents, but they need to communicate. Trigger.dev uses events to connect them.

Each job emits an event when it completes. The next job listens for that event. This creates a chain without hard dependencies or polling.

Update your main job file to emit events explicitly:

import { client } from "@trigger.dev/sdk/v3";

export const citationAgent = client.defineJob({
id: "citation-agent",
// ... existing code ...
run: async (payload, io, ctx) => {
// ... citation logic ...
const citations = [];

// Emit event for next agent
await io.sendEvent({
name: "citation-agent.completed",
data: { citations },
});

return { citations };
},
});

Each subsequent agent listens:

trigger: client.on({
event: "citation-agent.completed",
}),

This decouples your agents. If one fails, others aren't blocked. If you want to add a new agent later, you just listen for the right event.

Step 8: Implement Error Handling and Retries

Production systems fail. Network calls timeout. APIs return errors. Trigger.dev handles this with built-in retry logic, but you need to configure it.

Update your job definitions:

export const citationAgent = client.defineJob({
id: "citation-agent",
name: "Citation Tracking Agent",
version: "1.0.0",
trigger: client.cron({
cron: "0 */6 * * *",
}),
queue: {
concurrencyLimit: 10, // Limit parallel executions
},
run: async (payload, io, ctx) => {
try {
// Your agent logic
} catch (error) {
await io.logger.error("Citation agent failed", {
error: error instanceof Error ? error.message : String(error),
attempt: ctx.attempt,
maxAttempts: ctx.maxAttempts,
});

// Re-throw to trigger Trigger.dev's retry mechanism
throw error;
}
},
});

Configure retry behavior in your trigger.config.ts:

import { defineConfig } from "@trigger.dev/sdk/v3";

export default defineConfig({
project: "your-project-id",
runtime: "node",
logLevel: "log",
retries: {
enabledInProduction: true,
default: {
maxAttempts: 3,
minBackoff: 60, // 1 minute
maxBackoff: 3600, // 1 hour
factor: 2, // Exponential backoff
},
},
});

With this configuration:

• Failed tasks retry automatically
• Backoff increases exponentially (1 min, 2 min, 4 min)
• After 3 attempts, the job is marked as failed
• You're notified via webhook or dashboard

Step 9: Scale Your Agent Pipeline

Once your pipeline is working, you'll want to scale it. Trigger.dev handles this automatically, but you need to understand the constraints.

Concurrency Limits: By default, Trigger.dev runs multiple jobs in parallel. If your Citation Agent queries 5 engines × 20 keywords = 100 API calls, you want to control how many run simultaneously:

queue: {
concurrencyLimit: 20, // Max 20 parallel tasks
}

Batch Processing: Instead of processing one citation at a time, batch them:

const batchSize = 50;
for (let i = 0; i < citations.length; i += batchSize) {
const batch = citations.slice(i, i + batchSize);
await io.runTask(`process-batch-${i}`, async () => {
// Process entire batch
});
}

Database Optimization: As your pipeline generates more data, database writes become a bottleneck. Use batch inserts:

await io.runTask("batch-insert-metrics", async () => {
await database.metrics.insertMany(metrics, { ordered: false });
});

Monitoring and Alerts: Set up alerts for failures:

if (failureRate > 0.1) {
await io.sendEvent({
name: "high-failure-rate",
data: { failureRate, threshold: 0.1 },
});
}

Step 10: Deploy and Monitor Your Pipeline

You've built everything. Now deploy it to production.

First, ensure all your environment variables are set in Trigger.dev's dashboard:

npx trigger.dev env:set SEARCHFIT_API_KEY "your-key"
npx trigger.dev env:set OPENAI_API_KEY "your-key"
npx trigger.dev env:set ANTHROPIC_API_KEY "your-key"

Deploy all jobs:

npx trigger.dev deploy

Trigger.dev compiles your TypeScript, uploads it, and activates your jobs. Your Citation Agent will start running on its cron schedule (every 6 hours).

Monitor execution in the Trigger.dev dashboard:

• Logs: See real-time output from each agent
• Metrics: Track success rates, execution times, and failure patterns
• Events: Watch data flow between agents
• Webhooks: Receive alerts when jobs fail or complete

Set up a webhook to receive completion notifications:

// In your backend
app.post("/webhooks/trigger", (req, res) => {
const { event, data } = req.body;

if (event === "reporting-agent.completed") {
// Update your dashboard with new report
updateDashboard(data);
}

res.status(200).json({ ok: true });
});

Configure the webhook URL in Trigger.dev's settings.

Pro Tips for Multi-Agent Orchestration

Tip 1: Use Structured Data Throughout Each agent outputs structured data. The next agent expects that exact structure. Define TypeScript interfaces for every data flow. This prevents silent failures where one agent produces unexpected output.

Tip 2: Implement Circuit Breakers If an external API (like SearchFit's tracking API) is down, don't retry forever. Implement a circuit breaker:

let failureCount = 0;
const maxFailures = 5;

if (failureCount > maxFailures) {
await io.logger.warn("Circuit breaker open. Skipping API calls.");
return { skipped: true };
}

Tip 3: Cache Intermediate Results Content Analysis Agent fetches and analyzes competitor content. Cache these results. Don't re-analyze the same domains daily:

const cached = await cache.get(`analysis-${domain}`);
if (cached && Date.now() - cached.timestamp < 86400000) {
return cached.data; // Return cached result
}

Tip 4: Monitor Agent Dependencies If Content Analysis Agent depends on Citation Agent, track that dependency. If Citation Agent fails, Content Analysis won't have data. Log this clearly.

Tip 5: Use SearchFit's AI Tracking for Real-Time Visibility Instead of building your own visibility tracking, integrate with SearchFit's AI Tracking to monitor your brand across ChatGPT, Perplexity, Gemini, Claude, and Grok. This gives you authoritative data without building scrapers.

Key Takeaways

Multi-agent orchestration with Trigger.dev transforms how you approach answer engine optimization:

1. Specialization Wins: Each agent does one thing well. Citation Agent finds citations. Content Analysis Agent explains patterns. Content Generation Agent creates optimized content. This separation makes your system maintainable and scalable.

2. Event-Driven Architecture: Agents communicate through events, not direct calls. This decouples them. You can add, remove, or modify agents without rewriting others.

3. Automatic Orchestration: Trigger.dev handles scheduling, retries, error handling, and scaling. You write the agent logic. Trigger.dev handles the infrastructure.

4. Data-Driven Content: You're not guessing what AI engines want. You extract patterns from what already ranks, then engineer content to match those patterns. This is fundamentally different from traditional SEO.

5. Closed Feedback Loops: The Visibility Tracking Agent monitors whether your generated content actually ranks. This data feeds back into the next content generation cycle. You improve continuously.

6. Scale Without Breaking: Once your pipeline works, scaling is automatic. Trigger.dev handles 10 concurrent jobs or 10,000. Your code doesn't change.

This stack, Trigger.dev + multi-agent orchestration, is how modern AEO teams operate. You're not manually checking ChatGPT for citations. You're running automated agents that find citations, analyze patterns, generate optimized content, track visibility, and report results. All while you sleep.

Start with the Citation Agent. Get that working. Then add Content Analysis. Then Content Generation. Build incrementally. Each agent you add multiplies your capabilities.

The brands winning in AI search aren't the ones with the best writers. They're the ones with the best systems. This guide gives you that system.

Next Steps

You now understand the architecture. Here's what to do:

1. Set up your Trigger.dev project using the steps in this guide
2. Implement the Citation Agent first
3. Connect it to SearchFit's tracking to monitor real AI visibility
4. Build Content Analysis Agent once citations are flowing
5. Generate content with patterns you've learned
6. Track visibility of what you generate
7. Iterate based on what works

If you want to skip the engineering, SearchFit does all of this for you. Our platform automatically tracks your brand across ChatGPT, Perplexity, Gemini, Claude, Grok, and Google. We analyze what's ranking. We generate content engineered for AI visibility. We track whether it works. Visit SearchFit to see your AI visibility in action.

But if you want to build this yourself, you now have the blueprint. Follow these steps. Deploy your agents. Watch your AI visibility climb.