In our previous deep dives, we explored the architecture and optimization strategies behind Clawdbot—the advanced multi-agent system designed to dominate the era of Generative Engine Optimization (GEO). Now, it's time to build it.
This guide provides the specific technical instructions to construct Clawdbot using Claude 3.5 Sonnet, Exa AI, and Make.com (or Latenode). By following this pipeline, you will move from a linear automation to a collaborative squad of AI agents capable of rigorous technical research and high-authority publishing.
Phase 1: Define Your Agent Architecture
Before connecting APIs, you must define the "squad" that makes up Clawdbot. Single-agent systems often fail at complex tasks due to context window limitations and lack of self-correction. Clawdbot uses a Multi-Agent System (MAS) where distinct roles handle specific cognitive loads.
The Core Roster
| Agent | Model | Role |
|---|---|---|
| Lead Orchestrator | Claude 3.5 Sonnet | Holds master plan, delegates tasks, prevents context truncation |
| Researcher | GPT-4o-mini or Claude Haiku | Fetches and validates data using Exa API |
| Technical Writer | Claude 3.5 Sonnet | Writes in Markdown with KaTeX support |
| Critic | GPT-4o | Scores drafts 1–10, ensures quality control |
1. Lead Orchestrator (Claude 3.5 Sonnet)
Holds the master plan and delegates tasks. It prevents context truncation by maintaining the high-level strategy in memory while sub-agents handle the details.
System Prompt Example:
You are the Lead Orchestrator. Your job is to:
1. Decompose the user query into 3-5 sub-tasks
2. Assign each sub-task to the appropriate specialist agent
3. Maintain the master plan in your context
4. Synthesize final outputs from all agents
Never write the final content yourself.2. The Researcher (GPT-4o-mini or Claude Haiku)
A specialized tool-user. It does not write the final draft; it only fetches and validates data using the Exa API to ensure grounding.
3. The Technical Writer (Claude 3.5 Sonnet)
The prose specialist. It is instructed to write in Markdown with KaTeX support for mathematical precision.
4. The Critic
A quality control agent that scores drafts on a 1–10 scale. If a draft scores below 7, it loops back to the writer for revision, ensuring no hallucinated content reaches production.
Phase 2: Configure the Neural Research Layer (Exa API)
Clawdbot's "eyes" are powered by Exa (formerly Metaphor). Unlike standard Google searches which prioritize popularity, Exa uses embeddings-based search to find semantically relevant technical documentation.
Setup Instructions
1. Obtain API Keys
Register at the Exa dashboard to get your x-api-key.
2. Configure the API Call
In your HTTP request module (in Make or Latenode), set the endpoint:
POST https://api.exa.ai/search3. Optimize the Payload
{
"query": "transformer attention mechanism optimization techniques",
"useAutoprompt": true,
"numResults": 10,
"contents": {
"text": true
},
"category": "research paper"
}Critical Configuration:
| Parameter | Value | Why It Matters |
|---|---|---|
useAutoprompt | true | Lets Exa optimize your query automatically |
contents.text | true | Retrieves full page text (up to 100k chars) instead of snippets |
category | "research paper" or "company" | Filters out SEO spam, focuses on authoritative sources |
This allows the Researcher agent to actually "read" the papers it finds, not just skim snippets.
Phase 3: Build the Orchestration Pipeline (Make.com or Latenode)
You need a visual builder to manage the flow of data between your agents. Make.com is excellent for structured, linear flows, while Latenode is superior for complex logic involving JavaScript and long-running processes.
Step-by-Step Workflow
┌──────────────────────────────────────────────────────────┐
│ CLAWDBOT PIPELINE │
├──────────────────────────────────────────────────────────┤
│ │
│ [RSS/Webhook Trigger] │
│ │ │
│ ▼ │
│ [LlamaParse: PDF → Markdown] │
│ │ │
│ ▼ │
│ [Orchestrator: Task Decomposition] │
│ │ │
│ ┌────┴────┬────────┐ │
│ ▼ ▼ ▼ │
│ [Research] [History] [Specs] ← Parallel Execution │
│ │ │ │ │
│ └────┬────┴────────┘ │
│ ▼ │
│ [Writer Agent: Draft Generation] │
│ │ │
│ ▼ │
│ [Critic Agent: Score 1-10] │
│ │ │
│ ┌────┴────┐ │
│ ▼ ▼ │
│ [<7: Loop] [≥7: Continue] │
│ │ │ │
│ └──────────────┤ │
│ ▼ │
│ [Format: MD → HTML + Schema] │
│ │ │
│ ▼ │
│ [WordPress: Publish Draft] │
│ │
└──────────────────────────────────────────────────────────┘1. The Trigger
Start with a "Watch RSS Feed" module (e.g., tracking arXiv or TechCrunch) or a Webhook to catch new topics.
2. Data Intake
If the source is a PDF (like a research paper), use LlamaParse to extract clean Markdown text from the document. Standard text extractors often fail with complex layouts, but LlamaParse preserves structure.
// LlamaParse configuration
const parseConfig = {
input_format: "pdf",
output_format: "markdown",
preserve_tables: true,
extract_images: false
};3. Scenario Outputs
In Make.com, utilize the "Scenario Inputs/Outputs" feature. This ensures that when the Researcher Agent finishes, it passes a structured JSON object to the Writer Agent:
{
"sources": [
{
"url": "https://arxiv.org/abs/...",
"title": "Attention Is All You Need",
"summary": "Key findings...",
"citations": ["[1]", "[2]"]
}
],
"key_facts": ["Fact 1", "Fact 2"],
"entities": ["Transformer", "Attention Mechanism"]
}4. Parallel Execution
If using Latenode, you can spin up multiple sub-agents in parallel—one to research the history of the topic, one to find current competitors, and one to find technical specs—merging their outputs for the Writer.
Phase 4: Implement GEO and AEO Formatting Standards
For Clawdbot to succeed in 2026, it must optimize for Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO). This means writing for AI summarizers, not just human readers.
Hard-Code These Rules into the Writer Agent's System Prompt
WRITER AGENT SYSTEM PROMPT:
You are a technical writer optimized for GEO/AEO. Follow these rules:
1. QUICK ANSWER LEAD (Required)
- Start EVERY post with a 40-80 word direct answer
- This must answer the user's core intent immediately
- Format: "## Quick Answer" followed by the response
2. FACT-DENSITY (Required)
- Include minimum 3 unique statistics per section
- Use "answer nuggets" - concise factual statements
- No filler content or generic explanations
3. STRUCTURED DATA (Required)
- Generate FAQPage schema for any Q&A content
- Generate Article schema with all metadata
- Include entity relationships in content
4. FORMATTING
- Use Markdown with proper H2/H3 hierarchy
- Wrap math in KaTeX: $formula$
- Include comparison tables for any list contentThe "Quick Answer" Lead
Force the agent to start every post with a 40–80 word direct answer to the user's intent. This increases the chance of being picked up as a Featured Snippet or AI Overview.
Fact-Density
Instruct the agent to include unique statistics and "answer nuggets" at a high frequency. GEO algorithms prioritize content with high information gain.
Structured Data (Schema)
Automate the generation of JSON-LD schema, specifically FAQPage and Article schema:
{
"@context": "https://schema.org",
"@type": "TechArticle",
"headline": "{{title}}",
"author": {
"@type": "Organization",
"name": "Aiwalay"
},
"datePublished": "{{date}}",
"mainEntity": {
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "{{faq_question_1}}",
"acceptedAnswer": {
"@type": "Answer",
"text": "{{faq_answer_1}}"
}
}
]
}
}Phase 5: The Quality Control Loop and Publication
Automated content often fails the "sniff test." Clawdbot mitigates this with a rigorous review phase before anything goes live.
The Critic Loop Configuration
1. The Critic Node
Pass the Writer's draft to a separate LLM node (e.g., GPT-4o) with instructions to act as a harsh critic:
CRITIC AGENT SYSTEM PROMPT:
You are a harsh technical editor. Score this draft 1-10 based on:
1. ACCURACY (0-3 points)
- Are all claims cited?
- Any logical fallacies?
- Any hallucinated facts?
2. STRUCTURE (0-3 points)
- Does it start with Quick Answer?
- Is the hierarchy clear?
- Are tables/code blocks properly formatted?
3. VALUE (0-4 points)
- Is there high information gain?
- Any filler content to remove?
- Would this be cited by an AI system?
Output format:
{
"score": 7,
"issues": ["Issue 1", "Issue 2"],
"suggestions": ["Fix 1", "Fix 2"]
}2. Conditional Logic
Use a router in your workflow:
| Condition | Action |
|---|---|
| Score < 7 | Route back to Writer Agent with Critic's feedback |
| Score ≥ 7 | Proceed to formatting stage |
3. Final Polish
Use a transformer module to convert the Markdown to HTML if your CMS requires it. Ensure all mathematical formulas are wrapped in KaTeX syntax ($...$) for proper rendering.
4. Publishing
Use the WordPress API module to upload the post:
// WordPress publishing configuration
const publishConfig = {
endpoint: "https://yoursite.com/wp-json/wp/v2/posts",
method: "POST",
body: {
title: output.title,
content: output.html_content,
status: "draft", // Always draft for human review
categories: [output.category_id],
meta: {
_yoast_wpseo_metadesc: output.meta_description,
_yoast_wpseo_focuskw: output.focus_keyword
}
}
};Set the status to "Draft" for a final human review to ensure safety and compliance.
Complete System Checklist
Before going live, verify each component:
| Component | Status | Notes |
|---|---|---|
| Exa API Key | ☐ | Configured in Make/Latenode |
| LLM API Keys | ☐ | Claude + GPT-4o for Critic |
| LlamaParse | ☐ | For PDF ingestion |
| WordPress API | ☐ | With proper authentication |
| Critic Threshold | ☐ | Set to 7 minimum |
| GEO Prompt Rules | ☐ | Embedded in Writer agent |
| Schema Templates | ☐ | FAQPage + Article |
Final Note
This architecture moves beyond simple "prompt engineering" into "system engineering." By chaining specialized agents with specific tools (Exa, LlamaParse) and enforcing strict GEO standards, Clawdbot becomes a self-sustaining engine for authoritative technical content.
Ready to build your own Clawdbot? Contact our AI engineering team or explore our products to get started.