Learn how to quickly run, debug, and optimize agents for web data extraction and e-commerce automation. Includes step-by-step instructions, examples like Amazon scraping and multi-platform price comparison, and model tuning strategies using GPT-4

Quick Start Guide

Core Principle: For an easy start, run tasks first, then optimize based on the gap between results and expectations.

Step 1: Direct Start

• Go to the Run module, enter your user prompt, and start the Agent
• If you find the Agent's task results differ from your expectations, proceed to the next step

Step 2: Identify Problems (Define the Gap)

Step 3: Optimize System Instructions (Condition-Requirement-Constraint)

Instruction Structure:

1. Condition (When is this triggered?)
2. 1. Define the scenario clearly, e.g., "When accessing Amazon..."
2. Requirement (How to solve it?)
3. 1. Clearly define steps and output, e.g., "①Load all products→②Extract name/price→③Output CSV file"
3. Constraint (Optional - What absolutely cannot be done?)
4. 1. Define limitations and solutions, e.g., "Prohibited from clicking ads."

Step 4: Complete Practical Examples

Example 1: Amazon Product Data Extraction Agent

Initial Run (No System Instructions)

User Task Instructions:

Search Amazon for "wireless headphones" category, extract the first 10 products' names, prices, ratings, and links, then export as CSV file.

Problems Discovered:

• Missing price data (extraction error)
• Incorrect links (URL error)

Optimized User Task Instructions

Objective: Search Amazon for "wireless headphones", extract complete information for the first 10 products, complete within 30 steps.

Specific Requirements:
1. Search keyword: "wireless headphones"
2. Extract fields: Product Name, Price, Rating, Product Link
3. Product quantity: First 10 products
4. Output format: CSV file, column order as "Product Name,Price,Rating,Product Link"
5. Step limit: Complete within 30 steps
6. Data quality: Ensure each field has data or mark as "data missing"

Optimized System Instructions

You are a professional Amazon product data extraction specialist. You need to efficiently and accurately extract product information from Amazon website.

【Extraction Process Optimization】
When on Amazon product listing pages:
① Load complete listing page → ② Click each product card once in sequence → ③ Extract title/price/rating/link → ④ Return to previous page and continue until all specified products collected
Constraints: Prohibited from clicking advertisement positions, prohibited from repeatedly clicking same product

【Data Extraction Standards】
When entering Amazon product detail pages:
① Enter detail page → ② Read product title, current price, overall rating, product link in sequence → ③ If any field "name/price/rating/link" is missing, read page section by section and search once more; if still not found, mark as "data missing"
Constraints: Prohibited from estimating values to fill gaps; all data must reference actual page data

【Output Format Control】
When exporting all extraction results as CSV file:
① Compile all records → ② Generate CSV file with header row first, column order strictly as "Product Name,Price,Rating,Product Link" → ③ Ensure data integrity and format consistency
Constraints: Replace line breaks or extra commas in fields with spaces first, standardize price to numeric format

【Quality Assurance】
- Each product must include 4 fields: name, price, rating, link
- Price format: Keep only numbers and decimal points, remove currency symbols
- Rating format: X.X/5.0 or data missing
- Link format: Complete Amazon product URL

Example 2: Multi-Platform Price Comparison Agent

System Instructions

You are a professional e-commerce price comparison analyst. You need to search for the same product across multiple e-commerce platforms and conduct price and basic information comparison analysis.

【Platform Access Strategy】
When accessing different e-commerce platforms:
① Visit specified platforms in sequence → ② Search using same keywords → ③ Select most relevant product for data extraction → ④ Record platform name, product information, and price
Constraints: Select only 1 best matching product per platform, avoid staying too long on single platform

【Data Standardization】
When extracting data from different platforms:
① Standardize product name format (remove platform-specific identifiers) → ② Standardize price format (unified currency) → ③ Record platform-specific information (shipping methods, promotional info)
Constraints: Prices must be converted to numeric format, platform names use standard abbreviations

【Comparative Analysis】
When all platform data collection is complete:
① Organize all platform data → ② Calculate price differences and percentages → ③ Mark lowest and highest price platforms → ④ Generate comparison report
Constraints: Price comparisons must be based on same or similar products, note product differences

User Task Instructions

Task: Compare "iPhone 15 Pro 128GB" price information across Amazon, eBay, and Best Buy.

Execution Steps:
1. Visit Amazon, eBay, and Best Buy respectively
2. Search for "iPhone 15 Pro 128GB"
3. Select most matching product and extract information
4. Record: Platform name, product title, price, seller name, shipping info
5. Generate price comparison table
6. Mark best choice and price difference percentages

Output with csv file
Step Limit: Complete within 45 steps

Step 5: Model and Temperature Adjustment

Model Selection Guidelines:

Use GPT-4.1-Mini (10 credits/step):

• Simple, well-defined tasks
• Standardized data extraction
• Need quick, simple answers

Use GPT-4.1 (30 credits/step):

• Complex, uncertain tasks
• Multiple sites with different page structures
• Need to handle captchas or infinite scroll

Temperature Settings:

• Low (0-0.3): Specific information searches, consistent data extraction
• Medium (0.4-0.6): Balanced tasks requiring some flexibility
• High (0.7-1.0): Creative tasks like content generation, diverse outputs needed
  

Step 6: Complete Optimization Process Example

Full Optimization Workflow

Initial State:

• System Instructions: Empty
• User Task: Search Amazon for "laptop computers" first 5 products

First Run Results:

• Issues: Repeated clicks, missing prices, format chaos
• Steps: 45
• Success Rate: 60%

First Optimization: Added step control system instructions:

When on Amazon product listing pages:
① Load listing page → ② Click each product once in sequence → ③ Extract info then return → ④ Avoid repetitive operations

Second Run Results:

• Issues: Steps reduced, but data quality is still problematic
• Steps: 32
• Success Rate: 75%

Second Optimization: Added data quality control:

When extracting product information:
① Product name: Complete title → ② Price: Numeric format only → ③ Rating: X.X format → ④ Mark missing data as "N/A"

Third Run Results:

• Issues: Data quality improved, but output format still needs adjustment
• Steps: 28
• Success Rate: 90%

Final Optimization: Added output format control:

When exporting CSV:
① Header: Product Name,Price,Rating,Link → ② Data cleaning: Remove special characters → ③ Format validation: Ensure 4 fields per row

Final Results:

• Steps: 25 (44% reduction)
• Success Rate: 95%
• Output: Complete CSV file with proper formatting

Advanced Configuration Examples

E-commerce Price Tracker Agent

System Instructions: You are an e-commerce price tracking specialist...
Temperature: 0.2 (Low - for consistent data extraction)
Model: GPT-4.1-Mini (Cost-effective for structured tasks)

Content Research Agent

System Instructions: You are a content research analyst...
Temperature: 0.6 (Medium - for balanced analysis)
Model: GPT-4.1 (Better for complex reasoning)

Creative Content Generator

System Instructions: You are a creative content strategist...
Temperature: 0.8 (High - for diverse creative outputs)
Model: GPT-4.1 (Better for nuanced creativity)

Best Practices Summary

System Instructions Writing Tips:

1. Layered Structure: Group by functional modules (extraction, processing, output)
2. Specific Constraints: Clearly define what can and cannot be done
3. Exception Handling: Define how to handle missing data, page errors
4. Quality Standards: Set data format and completeness requirements

User Task Instructions Essentials:

1. Clear Objectives: Specific quantities, scope, format requirements
2. Step Limits: Reasonable step budgets
3. Quality Requirements: Data accuracy and completeness standards
4. Output Specifications: File format, naming, structure requirements

Common Pitfalls to Avoid:

• Overcomplicating initial instructions
• Adding constraints without testing first
• Ignoring cost vs. performance trade-offs
• Not specifying output requirements clearly
• Not handling edge cases (missing data, format variations)

Performance Optimization Tips:

1. Start Simple: Begin with basic functionality, add complexity gradually
2. Test Iteratively: Run → Identify issues → Optimize → Repeat
3. Monitor Costs: Balance model choice with budget constraints
4. Validate Outputs: Ensure data quality meets requirements
5. Document Changes: Track what works and what doesn't

Remember: The goal is to systematically bridge the gap between expectation and reality through optimization, not to create perfect instructions from the start.