Technology Adoption Decision Agent
A completed course project on its own GitHub branch. Not deployed.
A decision-support specialist that distinguishes adopt now, bounded experiment, proof of concept, monitor, defer and reject.
- Why
- I wanted the recommendation to follow available evidence and reversibility instead of binary optimism.
How I built it
- I encoded the six adoption dispositions in the specialist.
- I required application-level benefits, risks and missing evidence.
- I compared cases where proof of concept and bounded experiments differ.
- I ran response and scaffold validation checks.
- To copy it you need
- Pick a clearly bounded decision and list what evidence each adoption option would require.
Try it yourself
Copy this prompt and paste it into ChatGPT or Claude to try it yourself.
I want to build a small version of Jovan Diaz's Technology Adoption Decision Agent from his What Are We Building? profile. It is a course project, so treat it as a study exercise. It chooses among six options: adopt now, bounded experiment, proof of concept, monitor, defer or reject, based on evidence and how reversible the choice is. His gotcha: a successful proof of concept doesn't show that users, processes and operations are ready. Start with one clearly bounded decision, ask me what it is, and list the evidence each option would need. Ask me for anything you need.
Tools I used for this project
ChatGPT: I used ChatGPT to draft, test, critique or work through the workflow.
Codex: I used Codex to help build or revise repository files and tests.
Python: I used Python scripts or validation tools to process and check structured outputs.
GitHub: GitHub holds all of my repositories: code, instructions, changes and review history.
