I'm developing an AI-First Semantic SEO Article Writer and need to construct a comprehensive framework for AI agents to generate semantically optimized articles. This framework should encompass every aspect of the content creation process, from research and drafting to SEO optimization. Here's how the framework should be structured:

AI Agent Roles and Workflow:

Research Agent: Define how this agent will identify key topics, entities, and relevant data points. Specify how it analyzes competitors and top-performing content to gather key phrases, questions, and LSI terms.
Outline and Structuring Agent: Define a systematic approach for this agent to create an SEO-friendly article outline based on research. This includes structuring content for maximum topical relevance and keyword alignment.
Content Generation Agent: Outline how the writing agent will use the research and outline to create high-quality, engaging content. Define specific techniques for maintaining relevance, flow, and adherence to SEO standards (e.g., entity-based SEO and keyword variation).
Editing and SEO Optimization Agent: Detail how this agent will refine the article for readability, keyword usage, and structure. Include instructions for how to optimize for featured snippets, meta tags, and schema markup.
Big-Picture Strategy: Synthetically Filling in Details:

Establish how the AI agents will "fill in the big picture" by first identifying the overarching topic (based on user input or keyword analysis) and then synthetically generating deeper, more specific details.
Use a layered content generation approach: the AI should start with high-level ideas (primary topic) and then systematically break them down into more granular details (subtopics, LSI keywords, and supporting entities).
Data-Driven Content Decisions:

Instruct the AI agents on how to leverage real-time data (search trends, keyword volume, competitor analysis) to inform content strategy. Detail how data should influence everything from keyword selection to semantic relevance.
Ensure that agents prioritize user intent and align the content with specific search queries or behaviors.
Adaptability for Various Content Types:

Outline how the AI agents should adapt their strategies for different content formats (e.g., long-form articles, product reviews, FAQ pages).
Provide structure templates based on the content type (e.g., how-to articles with step-by-step headings, product reviews with pros/cons sections).
Search Engine Optimization Tactics:

Entity-Based Optimization: Guide AI agents to identify and focus on key entities within the topic, ensuring that each entity is supported with semantically related content.
Featured Snippets: Define strategies for optimizing for different types of featured snippets (paragraphs, lists, tables).
Schema and Metadata: Guide AI agents on incorporating schema markup (FAQ, product, article) and writing compelling metadata (title tags, meta descriptions) optimized for search.
Content Quality Control and Refinement:

Implement quality checks throughout the process. Agents should review content for clarity, depth, and SEO alignment.
Ensure AI agents balance keyword optimization with natural, user-friendly language.
Tone, Style, and Audience Personalization:

Specify how AI agents should tailor tone and style to different audience segments (e.g., technical vs. casual).
Provide guidelines for maintaining a consistent voice while adapting to specific audiences and content types.
Cross-Linking and External Linking Strategy:

Ensure that AI agents incorporate both internal links (to strengthen site structure) and external links to authoritative sources (to boost credibility).
Include a mechanism for prioritizing high-quality sources for external links.
In this framework, each AI agent should follow a logical progression from high-level content strategy down to detailed writing and SEO optimization, synthetically generating details at every step to create semantically rich, highly optimized articles.