🎓 AIIQM-WELL University · Builder · Module B5 of 5

Deploying Your First AI Tool
From Script to Product

⏱ 75–90 min
📋 Prerequisites: B4 complete, ReAct pattern understood
📊 Builder Level
Lesson Outcome: You can wrap a Python script in a simple interface so someone else can use it without writing code.
AIMY: Every tool you've built so far lives in the terminal. It works. But only if you can write Python. Deploying means taking that tool and putting it somewhere that a non-technical person can use it. A CLI interface. A web form. An API endpoint. The core logic stays the same — but now it's accessible to anyone. This is how tools become products.
ANALYZE

Deployment Concepts

1

Separate logic from interface

Your AI function is the logic. The way someone uses it — CLI, web form, API — is the interface. Keep them separate. Change the interface without rewriting the logic.
2

CLI arguments make scripts portable

python my_tool.py --input "text" --model "claude-3-haiku" lets someone control behavior from the command line. That's a CLI interface. No code editing required.
3

Configuration files scale to teams

A config.json file lets users customize behavior without changing code. API keys, model names, prompts — all in one file.
4

Error messages make tools reliable

When something goes wrong, don't crash silently. Tell the user what happened, why, and what to fix. Good error messages are what separate products from scripts.
INTEGRATE

Build a Deployable Tool

  1. Create project folder: mkdir ai-tool-v1 && cd ai-tool-v1
  2. Create config.json:
    { "model": "claude-3-haiku-20240307", "max_tokens": 256, "api_key": "YOUR_API_KEY_HERE", "tool_name": "Document Processor" }
  3. Create tool.py with argument handling:
    import anthropic import json import sys import argparse def load_config(config_file): try: with open(config_file) as f: return json.load(f) except FileNotFoundError: print(f"Error: Config file '{config_file}' not found.") sys.exit(1) def process_document(text, config): client = anthropic.Anthropic(api_key=config["api_key"]) message = client.messages.create( model=config["model"], max_tokens=config["max_tokens"], messages=[{"role": "user", "content": f"Summarize: {text}"}] ) return message.content[0].text if __name__ == "__main__": parser = argparse.ArgumentParser( description="AI Document Processor" ) parser.add_argument("--text", required=True, help="Text to process") parser.add_argument("--config", default="config.json", help="Config file") args = parser.parse_args() config = load_config(args.config) result = process_document(args.text, config) print("Result:", result)
  4. Update config.json with your actual API key.
  5. Test: python tool.py --text "Your sample text here"
  6. Create README.md with usage instructions:
    # AI Document Processor Usage: python tool.py --text "your text here" python tool.py --text "text" --config custom_config.json Config File: Edit config.json to change model, API key, etc. Requirements: pip install anthropic
PTR (PROOF THAT IT RUNS)
  • Your tool runs via CLI with --text argument.
  • Config file controls behavior without code changes.
  • README explains how to use the tool.

Common Mistakes

Hardcoding API keys
Never put your API key in the code. Always read it from config.json or environment variables. If you accidentally commit it, the key is compromised.
No error messages
If something fails, tell the user why. "Invalid config file" beats a cryptic traceback.
CHECKPOINT
  • You created a CLI tool with argument parsing.
  • You separated logic from configuration.
  • You documented how to use the tool.
  • Someone else can run your tool without writing code.
AIM COMMITMENT (BUILDER COMPLETION)

Analyze: You understood deployment as separating logic from interface — tools become products when anyone can use them.

Integrate: You built a complete deployable tool with configuration, CLI arguments, and documentation.

Manage: You can now build and share AI tools — not just scripts that only you can run. The world is open.

Builder Level Complete. You learned the three core concepts: Python that works, APIs that respond, automation that scales. You built a ReAct loop. You deployed a tool. Everything that comes next builds on this foundation. Welcome to the summit.