Building Django Applications with GitHub Copilot Agent Mode

How to make a Django application using GitHub Copilot

Santiago Hernandez demonstrates the use of GitHub Copilot agent mode and GPT-4.1 to automate Django project setup and view generation. The developer completed a functional password generator application in approximately three hours while documenting the process. This workflow relies on specialized prompt files and an AGENTS.md context layer to guide the AI.

Why This Matters

Technical agents like Copilot can automate boilerplate and basic logic, but they occasionally introduce unrequested features or suboptimal UI organization. Engineers must balance the speed of automated generation against the resource cost of token-heavy instruction files like AGENTS.md which increase context window usage. While agents can self-correct errors like missing app registrations, the review process for each AI-generated change remains a non-trivial task. The tradeoff between AI-driven rapid prototyping and the necessity for manual refactoring of UI and JavaScript logic is a critical consideration for modern engineering workflows.

Key Insights

• GitHub Copilot agent mode demonstrated autonomous error correction in settings.py, 2026.
• Contextual grounding using AGENTS.md provides LLMs with project-specific constraints and state awareness.
• The GitHub Copilot plugin for PyCharm was utilized by Santiago Hernandez to generate boilerplate and logic.
• Cryptographically secure generation using the Python secrets module ensures higher entropy than standard random libraries.
• Automated verification via curl allows agents to validate HTTP responses without manual browser interaction.

Working Examples

A Django view generated by GitHub Copilot that uses the secrets library for secure password generation.

from django.http import JsonResponse\nfrom django.views.decorators.http import require_GET\nimport secrets\nimport string\n@require_GET\ndef generate_password_view(request):\n try:\n  length = int(request.GET.get('length', 12))\n  if length < 1 or length > 128:\n   return JsonResponse({'error': 'Password length must be between 1 and 128.'}, status=400)\n except (TypeError, ValueError):\n  return JsonResponse({'error': 'Invalid length parameter.'}, status=400)\n alphabet = string.ascii_letters + string.digits + string.punctuation\n password = ''.join(secrets.choice(alphabet) for _ in range(length))\n return JsonResponse({'password': password})

Example of a .prompt.md file used to instruct the agent mode for project initialization.

# Django setup instructions\n- Install django packages\n- Verify installation\n- Create requirements.txt file\n- Create a new Django project named *PasswordGenerator*\n- Create a new Django app named *generator*

Practical Applications

• Use case: Rapid prototyping of utility tools like password generators using agent-driven prompt files stored in .github/prompts.
• Pitfall: Over-reliance on agent-generated UI often leads to disorganized JavaScript and poor layout that requires manual refactoring.
• Use case: Automated environment setup where the agent installs dependencies and verifies installations via pip and django-admin.

References:

• https://dev.to/santiagohebo/how-to-make-a-django-application-using-github-copilot-bee
• https://github.com/santiago177/PasswordGenerator