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Build Your First AI Agent in 30 Minutes: A Hands‑On Tutorial

1. What You’ll Build & Why It Matters

• Define the core problem: automating a repetitive data‑entry or content‑generation task with a lightweight AI agent.
• Preview the final result – an agent that reads a CSV, generates personalized email drafts, and saves them to a file.
• Explain the real‑world value: saving hours per week without needing a full‑scale LLM deployment.

2. Tools & Setup (No GPU Required)

• List the prerequisites: Python 3.10+, a free OpenAI API key (or local LLM via Ollama), and the `openai` + `pandas` libraries.
• Provide a one‑line pip install command and a quick environment variable setup for the API key.
• Show how to test the connection with a simple “Hello World” call to the chosen LLM.

3. Designing the Agent’s Workflow

• Break the task into three steps: read input → generate content → write output.
• Define a clear “system prompt” that instructs the LLM to act as a helpful assistant with a specific tone.
• Introduce a simple state machine (or just a function chain) to keep the agent’s logic transparent and debuggable.

4. Writing the Core Agent Code

• Walk through the Python function that reads a CSV row, constructs a prompt with placeholders, and calls the LLM.
• Show how to handle API errors (rate limits, timeouts) with a retry wrapper and exponential backoff.
• Include a code snippet that saves the generated text to a new column in the DataFrame and exports to Excel.

5. Adding a Simple Memory & Context

• Explain how to store previous outputs in a list so the agent can reference them (e.g., “don’t repeat this suggestion”).
• Demonstrate a minimal sliding‑window approach to keep the conversation under the token limit.
• Provide a practical example: the agent remembers the last three email subjects to avoid duplication.

6. Testing & Iterating on the Agent

• Run the agent on a sample dataset of 10 rows and manually review the quality of the outputs.
• Adjust the system prompt based on common mistakes (e.g., too verbose, missing placeholders).
• Show how to log each API call and output to a text file for easy debugging and iteration.