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How to Build a Custom AI Agent for Automated Content Curation (Step-by-Step Guide)

1. Define Your Curation Goals and Data Sources

• Identify the specific topics, keywords, and content types (articles, videos, social posts) you want the agent to collect.
• List all target sources: RSS feeds, APIs (Twitter, Reddit, YouTube), newsletters, and bookmarking tools like Pocket or Notion.
• Set clear filtering rules (e.g., only English content, minimum 500 words, published within the last 24 hours).

2. Set Up Your AI Agent Stack (Tools & Frameworks)

• Choose a language model: GPT-4o or Claude 3.5 Sonnet for summarization and relevance scoring.
• Select an agent framework: LangChain or AutoGen to orchestrate the scraping, filtering, and output pipeline.
• Decide on a lightweight runtime: Python script on a VPS, GitHub Actions (free tier), or a serverless function (AWS Lambda / Vercel).

3. Implement the Scraping & Ingestion Pipeline

• Use libraries like feedparser for RSS, requests + BeautifulSoup for static pages, and newspaper3k for article extraction.
• Add a deduplication step: store URL hashes in a local SQLite DB or Redis to avoid re-processing the same content.
• Implement rate limiting and polite crawling delays (respect robots.txt and set a 2–5 second delay between requests).

4. Build the AI Filtering & Scoring Layer

• Write a prompt that instructs the LLM to rate content on three axes: relevance (1–10), authority (1–10), and freshness (1–10).
• Combine scores into a composite “curation score” and keep only items above a configurable threshold (e.g., ≥ 24/30).
• Generate a one-paragraph summary and 3–5 key tags for each passing item using the same LLM call.

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