AI Agent Workflow

Dive deep into BlogShoot’s multi-agent AI architecture and learn how it generates high-quality content.

The 10-Agent System

BlogShoot uses a specialized multi-agent architecture where each agent has a specific role in the content creation pipeline.

Why Multiple Agents?

Better Quality: Each agent is optimized for its specific task Parallel Processing: Multiple agents work simultaneously Specialization: Agents become experts in their domain Quality Control: Multiple review stages ensure excellence

Agent Pipeline

Phase 1: Planning (60 seconds)

1. Research Agent

Role: Information gathering and fact-checking

Tasks:

• Searches reliable sources for topic information
• Gathers statistics, studies, and examples
• Verifies facts and current trends
• Identifies expert opinions

Input: Topic + Questionnaire Output: Research brief with sources

2. SEO Agent

Role: Keyword research and optimization strategy

Tasks:

• Identifies target keywords
• Analyzes search intent
• Researches competitor content
• Plans keyword placement

Input: Topic + Research brief Output: SEO strategy document

3. Outline Agent

Role: Content structure and organization

Tasks:

• Creates logical content flow
• Defines sections and subsections
• Plans heading hierarchy
• Allocates content to sections

Input: Research + SEO strategy Output: Detailed content outline

Phase 2: Creation (2-3 minutes)

4-6. Writing Agents (3 agents)

Role: Content generation

Tasks:

• Generate content for assigned sections
• Maintain consistent voice and tone
• Incorporate research and examples
• Follow SEO guidelines

Why 3 agents?

• Work in parallel for speed
• Each handles 2-3 sections
• Maintain consistency through shared context

Input: Outline section + Context Output: Draft content sections

7. Image Agent

Role: Visual content creation

Tasks:

• Analyzes content themes
• Generates AI images matching context
• Creates featured and in-content images
• Optimizes for web display

Input: Content outline + Key themes Output: Optimized images + Alt text

Phase 3: Refinement (30 seconds)

8. Editing Agent

Role: Content improvement and refinement

Tasks:

• Improves readability and flow
• Eliminates redundancy
• Strengthens transitions
• Enhances clarity

Input: Draft content Output: Refined content

9. Format Agent

Role: HTML structure and formatting

Tasks:

• Applies proper HTML tags
• Creates heading hierarchy
• Formats lists and quotes
• Adds image markup
• Ensures responsive structure

Input: Refined content + Images Output: Formatted HTML

10. Quality Agent

Role: Final quality assurance

Tasks:

• Checks for errors and inconsistencies
• Verifies SEO compliance
• Ensures brand voice consistency
• Validates links and images
• Confirms readability scores

Input: Formatted content Output: Final content + Quality report

Agent Communication

Agents communicate through a shared context system:

Questionnaire → Context Store ← All Agents
     ↓
Research Brief → Context Store
     ↓
SEO Strategy → Context Store
     ↓
Content Outline → Context Store

Each agent can:

• Read from context store
• Write to context store
• Access previous agent outputs

Quality Metrics

The Quality Agent evaluates content on:

Readability

• Flesch Reading Ease: 60+
• Average sentence length: <20 words
• Paragraph length: 2-4 sentences

SEO

• Keyword density: 1-2%
• Keyword in title: Yes
• Keyword in headings: 2-3x
• Meta description: Optimized

Completeness

• Word count target: Met
• All sections: Complete
• Images: Included
• Links: Validated

Brand Alignment

• Tone: Matches guidelines
• Voice: Consistent
• Style: Follows rules
• Terminology: Correct

Handling Edge Cases

Topic Too Broad

Detection: Research Agent flags insufficient focus Solution: Suggests narrower subtopic

Insufficient Data

Detection: Research Agent can’t find reliable sources Solution: Requests more specific topic or uses general knowledge

SEO Conflict

Detection: SEO requirements conflict with readability Solution: Prioritizes readability, suggests alternative keywords

Off-Brand Content

Detection: Quality Agent detects tone mismatch Solution: Editing Agent rewrites problematic sections

Customization Options

Agent Behavior

You can influence agent behavior through settings:

Research Depth

• Quick: Surface-level research
• Standard: Balanced approach
• Deep: Comprehensive research

SEO Focus

• Light: Natural keyword usage
• Moderate: Balanced optimization
• Aggressive: Maximum optimization

Content Style

• Informative: Educational focus
• Persuasive: Conversion-oriented
• Entertaining: Engagement focus

Agent Feedback Loop

Provide feedback on generated content:

1. Rate content quality (1-5 stars)
2. Specify what to improve
3. Agents learn from feedback
4. Future content improves

Performance Monitoring

Track agent performance in Analytics:

• Average generation time per agent
• Quality scores by agent
• Success rates
• User satisfaction ratings

Advanced: Agent Override

Pro users can provide specific instructions to agents:

Research Agent

Focus on case studies from 2023-2024
Prioritize data from [specific sources]

Writing Agents

Use storytelling approach
Include customer quotes
Emphasize ROI benefits

Behind the Scenes

Technology Stack

• LLM Models: GPT-4 and Claude 3.5
• Image Generation: DALL-E 3 and Stable Diffusion
• Orchestration: Custom agent framework
• Context Management: Vector database
• Quality Control: Ensemble validation

Agent Selection

Agents are selected based on:

• Content type
• Topic complexity
• Language
• Quality requirements

Future Developments

Coming soon:

• Voice Agent: Audio content generation
• Video Agent: Video script creation
• Social Agent: Social media adaptations
• Translation Agent: Multi-language content

Next Steps

• Creating Content
• Content Quality Best Practices
• Advanced Customization