Agentic AI In SEO: AI Agents & Workflows For Audit (Part 2) via @sejournal, @VincentTerrasi

Building on our previous exploration of Agentic SEO’s ideation capabilities, this article takes a closer look at the second pillar: Audit.

As promised, we’ll look at how AI agents can transform the SEO audit process by providing corrections and thorough analysis that would otherwise take hundreds of hours of manual work.

Traditional SEO audits are often time-consuming, involving multiple tools and manual reviews.

With Agentic SEO, however, this process can be streamlined through autonomous AI agents that identify problems and recommend and implement solutions in real time.

AI Agents For Advanced Site Analysis

Full Website Analysis With Real-Time Corrections

Agentic SEO transforms the review process by:

Comprehensive crawling: AI agents can systematically analyze entire websites, including hidden pages and dynamic content that traditional crawlers might overlook. Intelligent pattern recognition: Unlike rule-based tools, AI agents can detect patterns and anomalies that may indicate deeper structural issues across your site. Real-time remediation: As well as identifying problems, the agents can generate code fixes, content improvements, and structural adjustments that can be implemented immediately.

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Example: Firecrawl Demo

With advanced AI crawling, Firecrawl can meticulously analyze HTML structures, extract microformats, and provide detailed performance metrics, revealing critical areas that need optimization and might otherwise be missed.

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Example: Similar to tools like Cursor integrated with GitHub, Agentic SEO enables immediate application of code fixes.

When an issue is identified, the agent directly suggests optimized code changes, allowing seamless implementation through direct integration with your repository, ensuring rapid and error-free remediation.

I’m confident that OpenAI’s Codex and Google’s Jules will be equally effective for these tasks.

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Workflow Architecture For Effective Auditing

Similar to our idea workflows, audit workflows consist of specialized components.

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The audit workflow typically includes:

Data collection agents: These collect information from your site, competitor sites, and search engine results. Analysis agents: These specialize in identifying technical issues, content gaps, and optimization opportunities. Recommendation agents: They prioritize issues and suggest specific solutions based on potential impact. Implementation agents: Generate corrected code, optimized content, or step-by-step implementation guides directly.

Practical Use Cases

Technical SEO Auditing

AI agents excel at identifying technical issues that are often overlooked:

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The agent doesn’t just flag the problem. It provides contextual recommendations and implementation guidance.

Content Gap Analysis

Beyond traditional auditing, AI agents can identify content gaps by:

Analyzing competitive content structures. Identifying SERP features you’re missing. Discovering semantic relationships between existing content. Suggesting opportunities for content consolidation or expansion.

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Internal Linking Optimization

One of the most powerful applications is internal linking analysis:

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How To Build Your Audit Agent

Creating an effective audit agent requires:

A specialized knowledge base: Provide the agent with SEO best practices, Google guidelines, and industry-specific benchmarks. Tool integration: Connect the agent to existing tools such as Screaming Frog, Moz, and Semrush, or custom APIs for comprehensive data collection. Human-in-the-loop checkpoints: Despite automation, human expertise is still needed to validate critical recommendations.

Case Study: Ecommerce Site Optimization

In less than 30 minutes, our Agentic SEO Audit System identified 347 critical technical issues for a mid-sized ecommerce site with 15,000 product pages.

It generated optimized title tags and meta descriptions for underperforming pages. It discovered and mapped content gaps in product categories. It created a comprehensive action plan based on revenue impact.

Implementing these recommendations resulted in a 32% increase in organic traffic within 60 days.

Current Challenges And Limitations

Although powerful, Agentic SEO auditing does have its challenges.

Tool integration complexity: Connecting Agentic to all the necessary data sources requires technical expertise. For instance, setting up MCP (or Model Context Protocol) servers can be a challenging task. Evolving standards: Agents require regular updates to keep pace with changes in search engine algorithms.

Tools to Build Your Own SEO Audit Agent

Here are some practical tools to help you get started:

Open-Source Workflow Automation – n8n is a powerful, open-source automation tool that allows you to create complex workflows without coding. It’s ideal for orchestrating SEO tasks like crawling, data extraction, and reporting. Python Framework for Multi-Agent Systems – CrewAI enables the development of multi-agent systems in Python, allowing specialized agents to collaborate on tasks such as data collection, analysis, and implementation. Agentic AI Platform – DNG.ai (Draft & Goal) is a no-code platform designed to automate complex SEO workflows using specialized AI agents. It offers features like: Agentic Workflows: Automate tasks such as keyword optimization, content creation, and data analysis. Multi-Agent Collaboration: Coordinate multiple agents to handle large-scale projects efficiently. Integration with Over 20 Marketing Tools: Seamlessly connect with tools like Google Search Console, Google Ads, Google Analytics, and more.

Resources to Learn and Get Started

To improve your understanding and skills in building SEO audit agents, you can also explore these resources:

Reddit Guide on AI Agent Tools: A comprehensive guide discussing various tools suitable for beginners in AI agent development: Built This AI-Powered SEO Audit Tool YouTube tutorial: How I Automated an SEO Agency with 15 AI Agents

Summary: Agentic SEO Is A Fundamental Shift

Agentic SEO’s audit capabilities represent a fundamental shift in how we approach technical optimization.

By combining AI’s pattern recognition abilities with the strategic insight of human experts, we can create audit systems that are more comprehensive and actionable than traditional approaches.

In our next article, we’ll explore the final pillar of Agentic SEO: Generation. We will examine how AI agents can generate missing content, optimize existing assets, and scale content production while maintaining quality and relevance through the “SEO Expert in the Loop” approach.

Stay tuned, and experiment with these techniques to transform your SEO workflow!

More Resources:

Create Your Own ChatGPT Agent For On-Page SEO Audits Explaining Agentic SEO To The C-Level Introduction To LLMs For SEO With Examples

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Featured Image: Deemerwha studio/Shutterstock