Planet 1 • Top of Funnel

The Awareness Planet

Autonomous paid advertising with multi-platform support, adversarial quality control, and automatic A/B test generation. From hypothesis to winning ad, the system handles the thinking—you handle the publishing.

✓ Facebook Ads ✓ LinkedIn Ads ⏳ Google Ads ⏳ TikTok Ads

Specialized Agents.
Validated Outputs.

🎯

Strategy Agent

Role: Decides what to test
Input: Product context, beliefs, brand DNA, audience DNA
Output: Testable hypothesis with clear independent variable
Validation: Hypothesis Critic scores 0-10, rejects weak ideas

✍️

Creative Agent

Role: Creates ad variants
Input: Hypothesis, brand voice, platform specs
Output: Minimum 3 variants (headline, body, CTA, image prompt)
Validation: Alignment Critic ensures proper A/B isolation

📊

Analyst Agent

Role: Analyzes results
Input: Experiment metrics, hypothesis, context
Output: Conclusion (confirmed/refuted/inconclusive)
Validation: Statistical significance gates, confidence scoring

Why Three Agents?

Traditional systems use a single model for everything. GrowingLoop uses specialized agents: one focused on strategy and hypothesis generation, one on creative execution, one on data analysis. Each agent has its own prompt engineering, validation critics, and quality gates. The result: higher quality outputs at every stage.

Bad Ideas Never
Make It To Testing

Every hypothesis scored by critic agents. Every variant validated for proper A/B structure. Weak ideas rejected before you spend a dollar.

// Quality Control Flow
HYPOTHESIS GENERATION
1. Strategy Agent generates 5 hypothesis candidates
2. Hypothesis Critic scores each (0-10):
- Candidate A: 4.2/10 // Too vague
- Candidate B: 8.7/10 // Selected
- Candidate C: 6.1/10 // Not testable
- Candidate D: 7.9/10
- Candidate E: 5.5/10 // Confounding variables
3. Best hypothesis selected (> 7.0 threshold)
4. Apply revision suggestions if needed
VARIANT GENERATION
1. Creative Agent generates 7 variants (minimum 3)
2. Variant Alignment Critic validates A/B isolation:
- Variant 1: ✓ Differs only in tested variable
- Variant 2: ✗ Changes both tone AND image
- Variant 3: ✓ Proper isolation
- Variant 4: ✓ Proper isolation
- Variant 5: ✗ Confounding CTA change
- Variant 6: ✓ Proper isolation
- Variant 7: ✓ Proper isolation
3. Misaligned variants revised or rejected
4. Copy Quality Critic scores remaining variants
5. Top 2 selected for A/B test
Only high-quality experiments reach publication

The Cost of Bad Tests

A poorly designed A/B test wastes budget and generates false conclusions. Testing "red button vs blue button with different headline" teaches you nothing—you can't separate which variable caused the difference. The adversarial critics catch these issues automatically, before you waste ad spend on invalid experiments.

Native Support For
Facebook & LinkedIn

📘

Facebook Ads

Character Limits: 125 headline / 500 body
CTAs: Learn More, Shop Now, Sign Up, Download, Contact Us
Formats: Single Image, Carousel, Video, Collection
Objectives: Traffic, Conversions, Engagement, Awareness
Export: Complete setup instructions with targeting specs

💼

LinkedIn Ads

Character Limits: 100 headline / 600 body
CTAs: Learn More, Register, Download, Apply
Formats: Single Image, Carousel, Video, Message Ads
Targeting: Job Titles, Industries, Company Size, Skills
Export: Platform-specific professional tone and formatting

Platform-Specific Prompts

Each platform gets tailored agent prompts. Facebook prompts emphasize social proof and emotional appeals. LinkedIn prompts focus on professional value propositions and B2B messaging. The Creative Agent automatically adjusts tone, style, and structure based on the target platform.

Complete Publishing
Instructions

The system generates everything you need to publish ads: exact copy, image generation prompts, platform setup steps, budget recommendations. No guesswork—just execute.

// Example: Export Instructions
EXPERIMENT: EXP-001
Platform: Facebook Ads
Hypothesis: "Pain-focused messaging outperforms aspiration"
Budget: $50/day for 7 days ($350 total)
VARIANT A (Pain-Focused):
Headline: "Tired of Tracking Calories Manually?"
Body: "Stop the guesswork. Our fitness tracker automatically..."
CTA: Learn More
Image Prompt (DALL-E): "Woman 55-65, frustrated expression,
holding phone with calorie counting app, kitchen setting,
morning light, photorealistic"
VARIANT B (Aspiration-Focused):
Headline: "Become The Healthiest Version Of Yourself"
Body: "Transform your wellness journey. Our fitness tracker..."
CTA: Learn More
Image Prompt (DALL-E): "Woman 55-65, confident smile,
wearing fitness tracker, hiking trail, morning light,
photorealistic"
SETUP STEPS:
1. Create new campaign in Facebook Ads Manager
2. Objective: Traffic
3. Budget: $50/day, 7 day duration
4. Audience: Women 55-65, interests: health, fitness
5. Create Ad Set A with Variant A creative
6. Create Ad Set B with Variant B creative
7. Generate images using provided DALL-E prompts
8. Publish and monitor for 7 days
9. Export metrics to CSV after completion

What You Can Test

Creative Test

Test ad creative variations: image style, video vs static, color schemes, visual composition

Messaging Test

Test messaging approaches: pain-focused vs aspiration, features vs benefits, urgency vs FOMO

Audience Test

Test audience targeting: broad vs narrow, interest-based vs lookalike, demographic variations

Format Test

Test ad formats: single image vs carousel, video vs static, collection vs standard

Complete Performance
Visibility

REACH METRICS

• Impressions
• Clicks
• Click-Through Rate (CTR)

COST METRICS

• Total Spend
• Cost Per Click (CPC)
• Cost Per Acquisition (CPA)

CONVERSION METRICS

• Conversions
• Conversion Rate
• Return on Ad Spend (ROAS)

Statistical Observation Gates

The Analyst Agent won't declare a winner prematurely. Experiments must meet minimum thresholds:

• Minimum Runtime: 48 hours (capture day-of-week effects)
• Minimum Impressions: 1,000 per variant (statistical significance)
• Data Quality Check: No anomalies or tracking issues

If gates aren't met, the experiment continues collecting data. No premature conclusions, no wasted learning opportunities.

Learnings Feed The
Belief System

Every experiment result updates the hierarchical belief system. Insights from Awareness Planet automatically inform future hypothesis generation and propagate to sibling products.

// Example: Belief Update After Experiment
Experiment Concluded:
Hypothesis: "Pain messaging > Aspiration for women 55-65"
Result: CONFIRMED (+28% CTR for pain-focused)
Belief Intelligence Agent:
├─ Creates Product-Level Belief:
│ "Pain-focused messaging outperforms aspiration"
│ Confidence: 0.65 | Evidence: 1 experiment
├─ Tests on Sibling Product (Yoga Mat, same audience)
│ Result: CONFIRMED (+31% CTR for pain-focused)
└─ Promotes to Audience-Level Belief:
"Pain-focused messaging works for Women 55-65, Health segment"
Confidence: 0.72 | Evidence: 2 experiments
Future hypotheses prioritize pain-focused angles for this audience