An AI-native team designs its delivery system around safe, repeatable AI collaboration. Simply using AI means adding tools to an otherwise unchanged process.
Dimension
Uses AI tools
AI-native
Workflow
Individuals prompt as needed
Specifications, context, and tests guide agents
Control
Existing review absorbs output
Permissions and risk gates limit actions
Success
Tool adoption or generated code
Delivery quality, effort, and defects
People still own product choices, architecture, and released changes. The team maintains reusable instructions and evaluations, keeps project knowledge retrievable, and strengthens verification as generation becomes faster.
Rewriting in plainer words…
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What do permissions and risk gates look like in practice for a coding agent?Mentions least privilege, sandboxed execution, and human approval for high-risk actions such as touching production, secrets or schema migrations.
What you can say
An AI-native team builds its delivery process around AI working safely and repeatably, while a team that just uses AI adds tools to the same old process.
In the tool-using team, individuals prompt when they need to, and the existing code review has to absorb whatever comes out.
In an AI-native team, specs, project context and tests guide the agents, and permissions and risk gates limit what an agent is allowed to do.
The team keeps reusable instructions and evaluations, makes project knowledge easy to retrieve, and strengthens verification as generation gets faster.
People still own product choices, architecture and released changes, and success means better delivery quality, less effort and fewer defects, not tool adoption or generated code.
The cost is real upfront work on specs, tests and gates, and if verification doesn't keep pace, faster generation just ships defects faster.
Weak answers to avoid
Equates AI-native with heavy tool adoptionSeat counts and prompt volume describe adoption, not a changed process. Say how specs, context, tests and gates let agents work safely and repeatably.
Implies AI now makes the design callsPeople still own product choices, architecture and what ships. Say where humans decide and how the team holds that line.
Measures success by code or PRs generatedOutput can rise while quality falls. Tie success to delivery quality, effort and defects instead.
Leaves existing review to absorb the outputReview built for human output gets swamped. Say verification has to get stronger as generation speeds up, through tests and risk gates.
Interview lens
Likely follow-ups, what you can say, and the weak answers to avoid.
