Agentic AI: Using a Buzzword to Justify Premium Charges to Uninformed Buyers

 

Since my original post took off, quite a few of you have reached out asking for more detailed examples. So today, I’m diving into one of those examples from the previous post and unpacking it in greater depth. 

 

The Marketing Claim

"Our autonomous AI agent researches competitors, analyzes their strategies, and creates comprehensive reports with no human intervention."

The Reality: A Step-by-Step Breakdown

Step 1: User Interface (What the User Sees)

Frontend GUI:

• Clean, modern web interface with a simple text box
• User types: "Research Tesla's competitors in the EV market and analyze their strategies"
• Clicks "Generate Report" button
• Sees a progress bar with AI-sounding status updates:
  • "AI Agent initializing research protocols..."
  • "Autonomous discovery of competitive landscape..."
  • "Deep learning analysis in progress..."
  • "Generating strategic insights..."

User's Perception: A magical AI is thinking and researching autonomously.

---

Step 2: Backend Reality (What Actually Happens)

Phase 1: Query Understanding (First LLM Call)

INPUT: "Research Tesla's competitors in the EV market and analyze their strategies"

LLM CALL #1 - Query Parser:
PROMPT: "You are a business research task planner. Break down this user request into structured objectives and identify key entities. Return your response in JSON format with: primary_company, industry, competitors_to_research, analysis_types, and data_requirements."

LLM RESPONSE:
{
  "primary_company": "Tesla",
  "industry": "Electric Vehicle Market",
  "competitors_to_research": ["Rivian", "Lucid Motors", "Ford Lightning", "GM Ultium", "BYD", "Nio"],
  "analysis_types": ["financial_performance", "product_comparison", "market_positioning", "strategic_initiatives"],
  "data_requirements": ["revenue_data", "product_specs", "pricing", "market_share", "recent_news"]
}

System Processing:

1. Parse LLM JSON response
2. Validate competitor list against internal database
3. Cross-reference with available data sources
4. Generate data collection workflow based on requirements
5. Create execution plan with API endpoints and scraping targets

This looks like AI planning, but it's really: A sophisticated prompt that converts natural language into structured data, which then feeds into traditional programming workflows.

Phase 2: Data Collection (API Orchestration)

The system runs a predetermined workflow - not AI decision-making:

2.1 Company Information Gathering

FOR each competitor:
  - Call Clearbit API → company info, funding, employee count
  - Call LinkedIn API → executive team data
  - Call SEC API → financial filings
  - Call Patent API → recent patent applications
  - Web scraping → press releases, product pages

2.2 Financial Data Collection

FOR each competitor:
  - Yahoo Finance API → stock performance
  - Crunchbase API → funding rounds
  - Parse 10-K filings → revenue, R&D spending

2.3 Product Intelligence

FOR each competitor:
  - Scrape product specification pages
  - Parse pricing information
  - Collect customer review data (Google, Trustpilot APIs)
  - Monitor social media mentions (Twitter API)

Still no AI - just API calls, web scraping, and data parsing with traditional programming.

Phase 3: Data Processing (Standard Analytics)

3.1 Data Normalization

- Clean and standardize collected data
- Remove duplicates and inconsistencies
- Convert different data formats to common schema
- Handle missing values with predefined rules

3.2 Metrics Calculation

- Calculate market share percentages
- Compute growth rates from historical data
- Generate pricing comparison matrices
- Score competitive positioning using weighted algorithms

3.3 Pattern Recognition

- Run clustering algorithms on product features
- Perform sentiment analysis on customer reviews
- Calculate correlation coefficients between metrics
- Apply predefined business rules for strategic categorization

This is traditional data science - pandas, numpy, scikit-learn, not "AI agents."

---

Step 3: The LLM Finally Appears (Limited Role)

After all the data collection and processing, the system makes strategic LLM calls:

Call #2: Executive Summary Generation

PROMPT: "You are a business analyst. Based on this structured data about Tesla's competitors [INSERT PROCESSED DATA], write a 200-word executive summary focusing on key competitive threats."

INPUT: Clean, structured data tables
OUTPUT: Polished executive summary text

Call #3: Strategic Insights

PROMPT: "Analyze these competitive metrics and identify 3 key strategic patterns. Format as bullet points."

INPUT: Calculated metrics and trends
OUTPUT: Strategic bullet points

Call #4: Report Formatting

PROMPT: "Format this analysis into a professional business report structure with appropriate headings."

INPUT: All collected insights
OUTPUT: Well-formatted report sections

Total LLM Usage: ~4 strategic API calls - query parsing and text generation, not "autonomous AI research."

---

Step 4: Report Assembly (Template Engine)

4.1 Template Population

- Load predefined report template (docx/html)
- Insert generated text into appropriate sections
- Populate data tables with processed metrics
- Generate charts using matplotlib/D3.js
- Add company logos and styling

4.2 Quality Assurance

- Run spell-check and grammar validation
- Verify all data sources are cited
- Ensure charts render correctly
- Check report length meets requirements

---

Step 5: User Delivery (What the User Sees Again)

Final Interface:

• Progress bar completes: "AI Analysis Complete!"
• User downloads a polished 15-page PDF report
• Report includes charts, tables, executive summary, and strategic recommendations
• Delivery time: 3-5 minutes

User's Experience: "Wow, the AI generated this entire report autonomously!"

---

The Technical Architecture Reality

What Powers This "Agentic AI"

Core Components:

1. Workflow Engine (Apache Airflow/custom) - orchestrates the process
2. API Gateway - manages external service calls
3. Data Pipeline (ETL) - processes and cleans collected information
4. Template Engine - formats final output
5. 3-4 Strategic LLM Calls - for text generation only

Programming Languages & Tools:

• Python/Node.js - main backend logic
• SQL/NoSQL databases - data storage
• REST APIs - external integrations
• Pandas/NumPy - data processing
• Scrapy/Beautiful Soup - web scraping
• Jinja2/Handlebars - report templating
• OpenAI/Claude API - text generation (minimal usage)

The Execution Flow Truth

User Input → LLM Query Parser → Database Lookup → API Orchestrator 
    ↓
Data Collection (15+ APIs) → Data Processing Pipeline → Analytics Engine
    ↓
Strategic LLM Calls (3x) → Template Engine → PDF Generator → User

Time Breakdown:

• Data Collection: 55% of processing time
• Data Analysis: 25% of processing time
• LLM Calls (Query + Text Generation): 15% of processing time
• Report Formatting: 5% of processing time

---

Marketing vs. Reality

Marketing Claim	Technical Reality
"Autonomous AI Agent"	Predetermined workflow with conditional logic
"Deep Learning Analysis"	Standard statistical calculations and data processing
"AI-Powered Research"	API calls to existing databases and web scraping
"Intelligent Strategic Insights"	Rule-based analysis + template-generated text
"Revolutionary AI Technology"	Traditional software engineering + 3 LLM calls

---

The Bottom Line

This "agentic AI" system is actually:

75% Traditional Software Engineering:

• Database queries and API integrations
• Data processing and statistical analysis
• Web scraping and information parsing
• Template engines and report generation

15% Business Logic:

• Workflow orchestration
• Data validation rules
• Competitive analysis frameworks
• Quality assurance processes

10% Large Language Model Usage:

• Query understanding and task decomposition
• Text summarization and strategic insights
• Professional writing tone and report formatting

What You're Really Paying For

When companies charge premium prices for this "AI agent," you're paying for:

1. API Subscriptions - Access to data sources (70% of operational cost)
2. Software Development - The integration and processing pipeline
3. Business Intelligence - The analytical frameworks and metrics
4. User Experience - The polished interface and report templates
5. LLM API Calls - A small fraction for text generation

The "AI" is essentially a sophisticated data aggregation and formatting system with some natural language generation at the end.