Coding with LLMs often starts with speed: can we write or debug faster, get a first version of a test or script without starting from a blank page? However, after the first wave of adoption, you should ask yourself: how can we make our work reliable across teams using AI?
Agentic engineering answers that question: it gives people with different roles and repositories a common way to use AI, instead of leaving everyone to ad hoc prompting.
Agentic engineering answers that question: it gives people with different roles and repositories a common way to use AI, instead of leaving everyone to ad hoc prompting.
The gap is uneven usage
AI adoption rarely happens evenly. Some people use GitHub Copilot every day, others only occasionally. Some work in mature repositories, others in notebooks or locally. Some want to learn how to use ready-made prompts, agents, instructions or skills, others want to customise their configuration or even build the foundations themselves.
All of these profiles are valid, but they need different support, which is why we describe adoption through three groups:
- Takers need practical assets they can use right away.
- Makers need enough understanding to adapt those assets to their own project or team.
- Builders need the space and technical depth to create new agents, skills and scaffolding that others can reuse.
Give everyone the same training or the same demo, and one person gets excellent output, another gets something generic, and a third doesn't trust the tool enough to let it touch anything important.
