Inside the 16-Agent AI Architecture: How Pressonify Generates Press Releases

Most AI writing tools use a single large language model (LLM) to handle everything. You give it a prompt, it generates text, you're done.

The problem: One model can't be great at everything. Writing requires creativity. SEO requires technical precision. Fact-checking requires skepticism. Fraud detection requires pattern recognition.

Our solution: 16 specialized AI agents, each expert at one specific task, working in parallel to generate publication-ready press releases in 60 seconds.

Here's how we built it.

The Problem with Single-Model AI

Traditional Approach

User Input → GPT-4 → Press Release

What this looks like:
- User provides announcement details
- Single GPT-4 call generates entire press release
- User reviews and edits
- Manual SEO optimization
- Manual fact-checking
- Manual distribution

Time: 30-60 minutes
Quality: Inconsistent
Hallucinations: Common (AI makes up facts)
SEO: Requires manual optimization
Fraud risk: No automated detection

Why This Fails

1. Conflicting Objectives

Writing a press release requires balancing:
- Creativity (engaging headlines, compelling narrative)
- Accuracy (verified facts, no hallucinations)
- SEO (keywords, structured data, meta tags)
- Compliance (legal review, fraud detection)

A single model can't optimize for all of these simultaneously. If you tune it for creativity, you sacrifice accuracy. If you tune it for accuracy, you sacrifice engagement.

2. No Specialization

GPT-4 is a generalist. It's good at many tasks, but not the best at any specific task. You wouldn't hire one person to be your writer, SEO expert, fact-checker, and fraud analyst—so why use one AI model for all of these?

3. No Parallel Processing

With a single model, tasks run sequentially:
1. Generate press release (30 seconds)
2. Optimize SEO (30 seconds)
3. Fact-check (60 seconds)
4. Fraud screening (30 seconds)

Total: 150 seconds (2.5 minutes)

With parallel processing, all tasks run simultaneously:
Total: 40 seconds (the longest individual task)

The Multi-Agent Architecture

Our Approach

User Input → Agent Orchestrator → 16 Specialized Agents (parallel) → Final Press Release

Key innovation: Each agent is a specialist, optimized for one specific task. Agents run in parallel, coordinated by an orchestrator.

The 16 Agents

Layer 1: Content Generation (Parallel)

1. PR Generation Agent (📝)

• Task: Write initial press release draft
• Model: GPT-4-turbo
• System Prompt: "You are a professional PR writer with 15 years of experience writing press releases for tech companies. Write in AP Style. Be concise, factual, and engaging. Use active voice. Avoid jargon."
• Input: Company name, announcement type, key details
• Output: Structured press release with headline, subhead, body, quotes, boilerplate
• Specialization: Creativity and storytelling
• Processing time: 25 seconds

Why GPT-4-turbo: Best at creative writing, understands nuance and tone.

2. Headline Optimization Agent (💡)

• Task: Generate 10 headline variations, score for engagement
• Model: GPT-4-turbo
• System Prompt: "Generate 10 headline variations optimized for journalist engagement. Use power words. Keep under 80 characters. Prioritize clarity over cleverness."
• Input: Press release content
• Output: 10 headlines ranked by predicted engagement score
• Specialization: Engagement and clickability
• Processing time: 15 seconds

3. Quote Generation Agent (💬)

• Task: Generate realistic company quotes
• Model: GPT-4
• System Prompt: "Generate realistic executive quotes. Avoid corporate jargon. Sound human. Include forward-looking statements and context."
• Input: Company name, executive name/title, announcement details
• Output: 2-3 realistic quotes attributed to executives
• Specialization: Authentic voice
• Processing time: 10 seconds

Layer 2: Quality Assurance (Parallel)

4. Fact Verification Agent (✅)

• Task: Verify factual claims
• Model: GPT-4 + RAG (Retrieval Augmented Generation)
• 5-Layer Verification:
• Source Attribution: Every claim must have a source
• Entity Verification: Company names, people, products must exist
• Timeline Coherence: Dates and sequences must be logical
• Quantitative Validation: Numbers must be realistic and internally consistent
• Confidence Scoring: 0-100 score for each claim
• Input: Press release content
• Output: Flagged claims with confidence scores, suggested corrections
• Specialization: Skepticism and verification
• Processing time: 40 seconds

Why GPT-4 + RAG: GPT-4 for reasoning, RAG for checking against real data sources (company websites, Crunchbase, LinkedIn, news archives).

5. Anti-Hallucination Engine (🛡️)

• Task: Prevent AI-generated false information
• Model: Custom fine-tuned GPT-4
• 5-Layer Checks:
• Source Attribution Required: No unattributed claims
• Factual Claim Validation: Cross-reference with known data
• Entity Verification: Verify entities exist
• Timeline Coherence: Dates must be logical
• Confidence Scoring: Flag low-confidence claims
• Input: Press release content
• Output: Hallucination risk score (0-100), flagged sections
• Specialization: Preventing false claims
• Processing time: 30 seconds
• Success rate: 100% (zero hallucinations in production)

6. Fraud Detection Agent (🔒)

• Task: Identify scams, spam, and suspicious content
• Model: Custom fine-tuned GPT-4 + pattern recognition
• 4-Layer Detection:
• Pattern Analysis: Detect scam indicators (get-rich-quick claims, urgent language, unrealistic promises)
• Entity Validation: Verify company legitimacy (Crunchbase, Companies House, LinkedIn)
• Claim Verification: Check factual accuracy (funding amounts, partnerships, customer counts)
• Risk Scoring: 0-100 scale (0 = legitimate, 100 = scam)
• Input: Press release content, company domain
• Output: Risk score, flagged sections, recommended actions
• Specialization: Pattern recognition and fraud detection
• Processing time: 35 seconds
• Accuracy: 85% auto-approval rate, 0% false positives

Layer 3: SEO & Optimization (Parallel)

7. SEO Enhancement Agent (🔍)

• Task: Optimize for search engines
• Model: GPT-3.5-turbo (faster, cheaper for structured tasks)
• Output:
• Meta description (155 characters, keyword-optimized)
• Title tag (60 characters, keyword-optimized)
• Schema.org NewsArticle structured data
• OpenGraph tags (social sharing)
• Twitter Cards
• Canonical URL
• H1/H2/H3 hierarchy optimization
• Input: Press release content
• Output: Complete SEO metadata package
• Specialization: Technical SEO
• Processing time: 20 seconds

Why GPT-3.5-turbo: SEO optimization is structured and predictable. GPT-3.5-turbo is 50% faster and 90% cheaper than GPT-4, with no quality loss for this task.

8. Keyword Extraction Agent (🔑)

• Task: Identify primary and LSI keywords
• Model: GPT-3.5-turbo
• Output:
• 1 primary keyword
• 5-10 LSI (Latent Semantic Indexing) keywords
• Keyword density analysis
• Placement recommendations (headline, first paragraph, subheads)
• Input: Press release content
• Output: Keyword strategy
• Specialization: Keyword research
• Processing time: 15 seconds

9. Readability Agent (📖)

• Task: Calculate and improve readability scores
• Model: GPT-3.5-turbo + custom algorithms
• Metrics:
• Flesch-Kincaid reading level
• Average sentence length
• Passive voice percentage
• Jargon usage
• Output: Readability score (0-100), suggested improvements
• Specialization: Clarity and accessibility
• Processing time: 10 seconds

Layer 4: Distribution & Targeting (Parallel)

10. Media Intelligence Agent (🎯)

• Task: Identify relevant journalists
• Model: GPT-4 + vector search
• Database: 10,000+ verified journalist contacts
• Process:
• Analyze press release content
• Extract industry, beat, company stage
• Vector search journalist database
• Score fit based on recent coverage
• Rank top 20 journalists
• Input: Press release content
• Output: Ranked list of journalists with contact info, recent articles, suggested pitch angles
• Specialization: Journalist targeting
• Processing time: 25 seconds

11. Journalist Outreach Agent (✉️)

• Task: Generate personalized pitch emails
• Model: GPT-4
• Process:
• Read journalist's recent articles (last 30 days)
• Identify shared themes with press release
• Generate personalized subject line
• Generate personalized pitch email
• Suggest specific articles to reference
• Input: Press release, journalist profile
• Output: Personalized pitch email for each journalist
• Specialization: Personalization at scale
• Processing time: 20 seconds per journalist (parallelized)

12. Social Media Agent (📱)

• Task: Generate social media content
• Model: GPT-4
• Output:
• Twitter/X thread (5-7 tweets)
• LinkedIn post (professional tone)
• Facebook post (conversational tone)
• Instagram caption + hashtags
• Input: Press release content
• Output: Platform-specific social content
• Specialization: Platform-specific content adaptation
• Processing time: 15 seconds

Layer 5: Compliance & Legal (Parallel)

13. Legal Compliance Agent (⚖️)

• Task: Check for legal issues
• Model: Custom fine-tuned GPT-4
• Checks:
• Forward-looking statements (require disclaimers)
• Comparative claims (require substantiation)
• Superlatives (must be verifiable)
• Trademark usage (proper attribution)
• Copyright issues (image rights, quote permissions)
• Input: Press release content
• Output: Flagged legal issues, suggested disclaimers
• Specialization: Legal risk assessment
• Processing time: 25 seconds

14. Domain Verification Agent (🔐)

• Task: Verify company domain ownership
• Model: Custom validation system
• Process:
• Generate 6-digit OTP code
• Send to business email
• Verify MX records exist
• Validate email delivery
• Check domain blacklists
• Input: Company email domain
• Output: Verification status, verified badge
• Specialization: Identity verification
• Processing time: 2-3 minutes (user-initiated, runs async)

Layer 6: Analytics & Intelligence (Post-Publication)

15. Performance Tracking Agent (📊)

• Task: Track press release performance
• Model: Custom analytics system
• Metrics:
• Views, unique visitors
• Click-through rate
• Time on page, bounce rate
• Social shares (Twitter, LinkedIn, Facebook)
• Journalist opens (email tracking)
• Media pickups (Google News, syndication)
• Input: Published press release URL
• Output: Real-time analytics dashboard
• Specialization: Performance measurement
• Processing time: Real-time (continuous)

16. Media Monitoring Agent (📰)

• Task: Track media coverage and mentions
• Model: GPT-4 + web scraping
• Process:
• Monitor Google News for company mentions
• Track syndication pickups
• Identify journalist articles referencing press release
• Calculate earned media value
• Input: Company name, press release content
• Output: Coverage report, earned media value
• Specialization: Media monitoring
• Processing time: Continuous (checks hourly)

The Orchestrator: How Agents Work Together

Parallel Processing Architecture

from pydantic_ai import Agent
from pydantic import BaseModel
import asyncio

# Define agents
pr_agent = Agent("openai:gpt-4-turbo", result_type=PRContent)
seo_agent = Agent("openai:gpt-3.5-turbo", result_type=SEOMetadata)
fact_agent = Agent("openai:gpt-4", result_type=FactCheckResult)
fraud_agent = Agent("openai:gpt-4", result_type=FraudScore)

# Run agents in parallel
async def generate_press_release(user_input):
    # Layer 1: Content generation (parallel)
    pr_task = pr_agent.run(user_input)
    headline_task = headline_agent.run(user_input)
    quote_task = quote_agent.run(user_input)

    # Wait for Layer 1
    pr_content, headlines, quotes = await asyncio.gather(
        pr_task, headline_task, quote_task
    )

    # Layer 2: Quality assurance (parallel)
    fact_task = fact_agent.run(pr_content)
    anti_hallucination_task = anti_hallucination_agent.run(pr_content)
    fraud_task = fraud_agent.run(pr_content)

    # Layer 3: SEO (parallel with Layer 2)
    seo_task = seo_agent.run(pr_content)
    keyword_task = keyword_agent.run(pr_content)
    readability_task = readability_agent.run(pr_content)

    # Wait for Layers 2 & 3
    fact_check, hallucination_score, fraud_score, seo_data, keywords, readability = await asyncio.gather(
        fact_task, anti_hallucination_task, fraud_task,
        seo_task, keyword_task, readability_task
    )

    # Layer 4: Distribution (parallel)
    journalist_task = media_intelligence_agent.run(pr_content)
    social_task = social_media_agent.run(pr_content)

    journalists, social_content = await asyncio.gather(
        journalist_task, social_task
    )

    # Aggregate results
    final_press_release = {
        "content": pr_content,
        "seo": seo_data,
        "fact_check": fact_check,
        "fraud_score": fraud_score,
        "journalists": journalists,
        "social": social_content
    }

    return final_press_release

Key Architectural Decisions

1. Why PydanticAI?

• Type safety: Every agent has a defined output schema (Pydantic models)
• Validation: Outputs are validated automatically
• Debugging: Easy to trace which agent produced which output
• Testing: Mock individual agents in unit tests

2. Why Parallel Processing?

• Speed: 40 seconds (parallel) vs 150 seconds (sequential)
• Efficiency: Maximize GPU utilization
• Cost: Pay for longest task, not sum of all tasks

3. Why Different Models for Different Tasks?

• Performance: GPT-4-turbo for creative tasks, GPT-3.5-turbo for structured tasks
• Cost: GPT-3.5-turbo is 90% cheaper for SEO and keyword tasks
• Speed: GPT-3.5-turbo is 50% faster for simple tasks

Cost Analysis

Per Press Release

• Layer 1 (Content): GPT-4-turbo × 3 agents = €0.12
• Layer 2 (Quality): GPT-4 × 3 agents = €0.18
• Layer 3 (SEO): GPT-3.5-turbo × 3 agents = €0.03
• Layer 4 (Distribution): GPT-4 × 2 agents = €0.08
• Layer 5 (Compliance): GPT-4 × 1 agent = €0.04

Total AI cost per press release: €0.45

Infrastructure costs (hosting, database, email): €0.15 per release

Total cost: €0.60 per press release

Pricing: €99-€399 per release

Margin: 99.4%-99.85%

Comparison: Traditional Approach

• Freelance writer: €400-€800 (labor cost)
• PR agency: €2,000-€5,000 (labor cost)
• Single-model AI: €0.05 (AI cost) + 30-60 minutes human time

Performance Benchmarks

Speed

• Multi-agent system: 40 seconds (average)
• Single-model system: 150 seconds (sequential processing)
• Human writer: 4-6 hours
• Improvement: 50x faster than manual, 3.75x faster than single-model

Quality

• Hallucination rate: 0% (zero false claims in production)
• Fraud detection accuracy: 85% auto-approval, 0% false positives
• SEO performance: 71% appear in Google News (vs 23% industry average)
• Journalist engagement: 3x higher response rate vs unverified platforms

Cost

• Multi-agent system: €0.60 per release (AI + infrastructure)
• Single-model system: €0.05 per release (AI only, no quality assurance)
• Human writer: €400-€5,000 per release (labor)
• Savings: 57%-95% vs traditional approaches

The Future: 32-Agent System

We're currently developing version 2.0 with 32 agents:

New Agents (Q1 2026)

• Investor Targeting Agent: Identify relevant VCs and angels
• Competitor Analysis Agent: Track competitor announcements
• Trend Prediction Agent: Predict industry trends from press releases
• Multi-Language Agent: Translate press releases (10 languages)
• Video Generation Agent: Create video summaries
• Podcast Script Agent: Generate podcast-ready scripts
• Crisis Detection Agent: Identify potential PR crises early
• Sentiment Analysis Agent: Track public sentiment in real-time

Performance Targets

• Speed: 30 seconds (25% faster)
• Cost: €0.40 per release (33% cheaper)
• Quality: 95% auto-approval rate (up from 85%)

Get Started

Experience Pressonify's PydanticAI-powered press release platform.

Try Pressonify today: pressonify.ai/generate

Pricing: €99 (Launch), €199 (Growth), €399 (Scale)

Anna Doran is Head of Product at Pressonify.ai, where she leads development of the world's first multi-agent AI system for press releases. She previously built AI infrastructure at Google DeepMind and led engineering at Scale AI.