Multi-Agent AI Tutorial: From Zero to Automated Research Pipeline (2026)

By the end of this tutorial, you'll have a 3-agent research pipeline that takes a topic and returns a structured research report -- automatically.

No fluff. No theory. Just a working multi-agent AI system you can build in under 30 minutes.

If you've been looking for a practical multi-agent AI tutorial that actually shows you how to build AI research pipelines end-to-end, this is it. We'll walk through creating three specialized AI agents -- a Researcher, an Analyst, and a Writer -- then wire them into a sequential pipeline that runs on autopilot.

Table of Contents

• What You'll Build
• Prerequisites
• Step 1: Create Your Ivern AI Account
• Step 2: Add Your API Keys (BYOK Setup)
• Step 3: Create the Research Agent
• Step 4: Create the Analysis Agent
• Step 5: Create the Writer Agent
• Step 6: Connect the Pipeline
• Step 7: Run It
• Cost Breakdown
• Next Steps and Related Guides

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What You'll Build

Here's the pipeline architecture:

Topic Input → Research Agent (find sources) → Analysis Agent (extract insights) → Writer Agent (compose report) → Structured Research Report

Each agent has one job. The Research Agent finds relevant sources and gathers raw data. The Analysis Agent extracts key insights, identifies patterns, and flags contradictions. The Writer Agent takes those insights and composes a clean, structured report.

Example Output

Give the pipeline this topic: "State of WebAssembly adoption in 2026"

You'll get back a structured report like:

Research Report: State of WebAssembly Adoption in 2026
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

EXECUTIVE SUMMARY
WebAssembly has crossed into mainstream adoption with 78% of
enterprise development teams using it in production...

KEY FINDINGS
1. Browser support is now universal across all major browsers
2. Server-side Wasm (WASI) adoption grew 340% year-over-year
3. Language ecosystem: Rust leads at 42%, followed by C++ at 28%

SOURCE ANALYSIS
- 12 sources analyzed across MDN, Chrome Developers, and academic papers
- 3 conflicting claims identified and noted
- Data recency: all sources within last 6 months

RECOMMENDATIONS
Based on current trends, teams should evaluate Wasm for...

That's the power of a multi-agent tutorial that actually works. Now let's build it.

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Prerequisites

You need three things before we start:

Requirement	Details	Cost
Ivern AI account	Free to create at ivern.ai/signup	Free
OpenAI API key	Used by your agents for LLM inference. GPT-4o-mini recommended.	~$0.02-0.05 per pipeline run
A topic to research	Any topic you want automated research on	Free

That's it. Ivern AI uses a bring your own key (BYOK) model, which means you pay only for the API calls you make -- no per-seat fees, no usage markup. This pipeline costs roughly $0.02-0.05 per run depending on your model choice and topic complexity.

If you already have an OpenAI API key, you're ready to go. If not, you can get one at platform.openai.com in about two minutes.

Already have experience with AI agents? Skip to Step 3 and start building. If you're new to multi-agent systems, check out our AI research assistant guide first.

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Step 1: Create Your Ivern AI Account

Head to ivern.ai/signup and create a free account. No credit card required.

The signup process takes about 30 seconds. You'll get access to the agent builder, pipeline designer, and dashboard immediately.

Once you're in, you'll land on the dashboard. This is your command center for managing agents, pipelines, and runs.

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Step 2: Add Your API Keys (BYOK Setup)

Ivern AI's BYOK model means you bring your own API keys. Your keys are encrypted at rest and never shared. Here's how to add them:

1. Navigate to Settings → API Keys
2. Click Add Key
3. Select OpenAI as the provider
4. Paste your OpenAI API key (starts with sk-...)
5. Give it a label like "GPT-4o-mini key"
6. Click Save

Your key is now available for any agent to use. You can add keys from multiple providers -- Anthropic, Google, Mistral, and others -- and assign different keys to different agents. This is one of the biggest advantages of the BYOK approach: you pick the best model for each agent's job.

For this multi-agent AI tutorial, we'll use a single OpenAI key with GPT-4o-mini for all three agents to keep costs minimal.

Want to explore free options first? See our guide to free AI agent tools.

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Step 3: Create the Research Agent

The Research Agent is the first agent in your pipeline. Its job is straightforward: given a topic, search for relevant sources and return structured raw findings.

Agent Configuration

From the dashboard:

1. Click Create Agent
2. Name it: Research Agent
3. Configure as follows:

Agent Name: Research Agent
Model: gpt-4o-mini
API Key: your-openai-key-label

System Prompt: |
  You are a research specialist. Given any topic, your job is to:
  1. Identify the most relevant and authoritative sources
  2. Extract key facts, statistics, and claims from each source
  3. Note the source, date, and reliability for each finding
  4. Return all findings in a structured JSON format

  Output format:
  {
    "topic": "<topic>",
    "sources_reviewed": <number>,
    "findings": [
      {
        "claim": "<the claim or fact>",
        "source": "<where it came from>",
        "confidence": "<high|medium|low>",
        "date": "<publication date>"
      }
    ],
    "gaps": ["<topics that need more research>"]
  }

Temperature: 0.3
Max Tokens: 4000

4. Click Save Agent

Why temperature 0.3? Research requires accuracy over creativity. A low temperature keeps the agent focused on factual extraction rather than creative interpretation.

The Research Agent is now ready. When it receives a topic like "WebAssembly adoption 2026," it will scan for relevant information, extract key claims, and return everything in a structured format that the next agent can consume.

For a deeper dive into building research agents specifically, see our how to build an AI research agent guide.

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Step 4: Create the Analysis Agent

The Analysis Agent receives the raw findings from the Research Agent and turns them into insights. It identifies patterns, flags contradictions, and ranks findings by significance.

Agent Configuration

1. Click Create Agent again
2. Name it: Analysis Agent
3. Configure:

Agent Name: Analysis Agent
Model: gpt-4o-mini
API Key: your-openai-key-label

System Prompt: |
  You are a data analyst specializing in synthesis and pattern recognition.
  You receive structured research findings and must:

  1. Group related findings into themes
  2. Identify patterns and trends across sources
  3. Flag any contradictions between sources
  4. Rate the strength of each theme (strong/moderate/weak evidence)
  5. Highlight the 3-5 most significant insights

  Output format:
  {
    "themes": [
      {
        "theme_name": "<name>",
        "findings_count": <number>,
        "strength": "<strong|moderate|weak>",
        "summary": "<2-3 sentence synthesis>"
      }
    ],
    "contradictions": [
      {
        "topic": "<what the contradiction is about>",
        "position_a": "<first claim>",
        "position_b": "<conflicting claim>"
      }
    ],
    "top_insights": ["<insight 1>", "<insight 2>", ...],
    "confidence_score": <0-100>
  }

Temperature: 0.2
Max Tokens: 3000

4. Click Save Agent

The Analysis Agent is the brain of your pipeline. It does the heavy lifting of making sense of raw research data. Where the Research Agent collects, the Analysis Agent thinks.

Notice the even lower temperature (0.2). Analysis demands precision -- we don't want the agent hallucinating patterns that don't exist in the data.

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Step 5: Create the Writer Agent

The Writer Agent takes analyzed insights and turns them into a polished, human-readable research report. This is the final agent in the pipeline.

Agent Configuration

1. Click Create Agent one more time
2. Name it: Writer Agent
3. Configure:

Agent Name: Writer Agent
Model: gpt-4o-mini
API Key: your-openai-key-label

System Prompt: |
  You are a technical writer who produces clear, structured research reports.
  You receive analyzed research data and produce a final report.

  Your report must include:
  1. Executive Summary (3-4 sentences)
  2. Key Findings (numbered list, each with supporting evidence)
  3. Source Analysis (credibility notes and data recency)
  4. Contradictions (any conflicting claims noted)
  5. Recommendations (actionable next steps based on findings)

  Writing guidelines:
  - Use clear, direct language
  - Lead with the most important findings
  - Cite sources inline
  - Keep paragraphs to 2-3 sentences max
  - Use bullet points for lists of 3+ items

  Write for a technical audience who needs accurate information fast.

Temperature: 0.5
Max Tokens: 3000

4. Click Save Agent

Here we bump the temperature to 0.5. Writing benefits from slightly more natural, flowing language. Not creative enough to hallucinate -- but enough to produce readable prose instead of robotic bullet lists.

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Step 6: Connect the Pipeline

Now the fun part. You have three agents. It's time to wire them together into a sequential pipeline where the output of each agent feeds directly into the next.

Pipeline Configuration

1. Navigate to Pipelines → Create Pipeline
2. Name it: Research Pipeline
3. Set pipeline type to Sequential
4. Add your three agents in order:

Pipeline Name: Research Pipeline
Type: Sequential

Step 1:
  Agent: Research Agent
  Input: {{user_topic}}
  Output Variable: raw_findings

Step 2:
  Agent: Analysis Agent
  Input: {{raw_findings}}
  Output Variable: analyzed_insights

Step 3:
  Agent: Writer Agent
  Input: {{analyzed_insights}}
  Output Variable: final_report

5. Click Save Pipeline

The pipeline flow looks like this:

User enters topic
       ↓
┌──────────────────┐
│  Research Agent   │  Finds sources, extracts claims
└────────┬─────────┘
         ↓
┌──────────────────┐
│  Analysis Agent   │  Groups themes, identifies patterns
└────────┬─────────┘
         ↓
┌──────────────────┐
│   Writer Agent    │  Composes structured report
└────────┬─────────┘
         ↓
  Final Research Report

Each agent automatically receives the output of the previous agent. The {{variable}} syntax tells Ivern AI how to pass data between steps. No custom code. No webhooks. Just clean configuration.

For more advanced pipeline patterns including parallel and conditional routing, see our guide to multi-agent AI pipeline sequential workflows.

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Step 7: Run It

Your pipeline is live. Let's test it.

1. Go to your Research Pipeline page
2. Click Run Pipeline
3. Enter your topic, for example: State of WebAssembly adoption in 2026
4. Click Start

Here's what happens:

• Research Agent runs first (typically 10-20 seconds). It searches for sources, extracts claims, and returns structured findings.
• Analysis Agent runs second (8-15 seconds). It receives those findings, identifies themes, flags contradictions, and ranks insights.
• Writer Agent runs last (8-15 seconds). It takes the analysis and composes your final report.

Total pipeline time: 30-60 seconds. Total cost: roughly $0.02-0.05.

The final report appears on your dashboard. You can download it, copy it, or have it sent to your email automatically.

Pro Tips for Better Results

• Be specific with topics. "WebAssembly adoption in enterprise backend services 2026" produces better reports than just "WebAssembly."
• Iterate on prompts. If the Research Agent misses sources you'd expect, refine its system prompt to include specific source types or domains.
• Use different models per agent. You could use GPT-4o for the Analysis Agent (where reasoning depth matters most) and GPT-4o-mini for the other two to balance quality and cost.
• Run variations. Change the topic slightly and run again to see how the findings compare.

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Cost Breakdown

One of the biggest advantages of building with Ivern AI's BYOK model is cost transparency. Here's exactly what this pipeline costs per run:

Agent	Model	Input Tokens	Output Tokens	Cost per Run
Research Agent	GPT-4o-mini	~1,500	~2,000	~$0.01
Analysis Agent	GPT-4o-mini	~2,000	~1,500	~$0.01
Writer Agent	GPT-4o-mini	~1,500	~2,000	~$0.01
Total per run				~$0.03

That's roughly $0.03 per complete research report. Run it 100 times and you're still under $3.00.

Compare that to hiring a research assistant or using per-seat SaaS tools that charge $50-200/month regardless of usage. With BYOK, you pay for exactly what you use -- nothing more.

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Next Steps and Related Guides

You now have a working multi-agent AI research pipeline. Here's where to go from here:

• Scale it up -- Add a fourth agent that fact-checks the Writer Agent's output for an extra quality layer
• Automate it -- Set up scheduled runs on topics you track regularly
• Expand the agents -- Give the Research Agent web browsing capabilities for real-time source discovery
• Try parallel workflows -- Run multiple Research Agents on subtopics simultaneously, then merge results

Related Guides

• How to Build an AI Research Agent -- Deep dive into building a single research-focused agent
• Multi-Agent AI Pipeline Sequential Workflows -- Advanced pipeline patterns beyond sequential chains
• How to Create an AI Agent Pipeline Step by Step -- Broader guide to pipeline design patterns
• Free AI Agent Tools -- No-cost tools to get started with AI agents
• AI Research Assistant: How It Works -- Understanding the architecture behind AI research systems

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Ready to Build Your First AI Agent Pipeline?

You've seen the full tutorial. You know the architecture. You know the cost. The only thing left is to build it.

Sign up for Ivern AI -- it's free and follow the steps above. You'll have a working multi-agent research pipeline in under 30 minutes. No credit card required. Bring your own API key and pay only for what you use.

Stop reading about AI agents. Start building them.