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Build a Custom AI Agent in Python: Automate Web Research & Email Reports

What We're Building & Why AI Agents Are a Game-Changer

We're building a Python AI agent that takes a topic, searches the web, summarizes the top results, and emails a formatted report.
Understand the core loop: LLM reasoning (planning) -> Tool Execution (search) -> LLM Synthesis (understanding) -> Action (email).
Practical applications: Automate competitor analysis, generate daily news digests, or monitor brand mentions without manual effort.

Python 3.10+ installed, along with a virtual environment to manage dependencies.
API keys: An OpenAI API key (for the LLM brain) and a SerpAPI key (for web search). Store them in a .env file for security.
Core libraries installed via pip: openai, serpapi, python-dotenv.

Create a web_search(query) function using the SerpAPI library to fetch the top 5 search results for a given query.
Create a summarize_content(text, topic) function using OpenAI's gpt-4o-mini model to condense the search results into a concise summary.
Create a send_email_report(recipient, subject, body) function using SMTP or SendGrid to deliver the final summary to a specified inbox.

Design the main agent_loop(user_query) function, starting with a system prompt that clearly defines the available tools and their JSON schema.
Implement structured output: The LLM must return a JSON action (e.g., {"tool": "web_search", "parameters": {"query": "latest AI news"}}) or a final answer.
Write the loop logic: Parse the JSON action -> Execute the matching Python function -> Feed the result back to the LLM for the next step, repeating until a final answer is reached.

Run the script with python agent.py and test with a query like “Find the latest trends in generative AI for healthcare”.
Add robust error handling: try/except blocks for API timeouts, invalid JSON parsing from the LLM, and missing environment variables.
Implement logging (using the logging library) to print the agent's internal “thought” process for debugging and transparency.