Most teams talking about agentic development are still doing something much simpler:
AI per developer.
That already helps a lot. We use tools like Codex, Claude Code, and OpenCode, and the productivity gain is real.
But local AI assistance is not the same thing as an agentic delivery workflow.
That is the distinction I have been thinking about lately in the context of XPER Consulting DocEngine.
To make it easier to talk about, I started using a simple five-level maturity model:
- AI assistant per developer
- Shared context and repository instructions
- Workflow-based agents around issues and PRs
- Coordinated specialized agents
- Self-correcting autonomous delivery loops
What matters now is the move from Level 1 to Level 3.
That is where AI stops being just a coding helper in the IDE and starts becoming part of the actual delivery flow:
- issue-driven work,
- shared repository instructions,
- automated checks,
- draft PR creation,
- human review and approval.
That feels like the next realistic step for us.
Not a science-fiction loop where agents independently run the company. Just a more structured and repeatable way to move work from issue to draft PR.
The difference is important.
An AI assistant helps one person in one moment. A delivery workflow creates shared memory and shared expectations. It knows where the work starts, what definition of done means, which checks must pass, and where a human must approve the result.
That is much more valuable than a clever autocomplete.
So my current view is this:
The next big gain in AI-assisted development will come from better workflow integration.
The interesting question is not only “which model is best?” It is also:
- Where does the agent get context?
- What artifacts does it produce?
- How is quality verified?
- How does the team review and reject work?
- How does the system learn from mistakes?
That is where agentic development becomes engineering instead of a demo.