AI-native platform
Models from the portal, agent teams on golden paths, and why engineers still sign off.
- Models from the portalsame approvals, same cost tracking
- Agent teamson golden paths
- Code-review agentson every pull request
- Engineers sign offevery change to production
AI makes the platform matter more, not less
More change now comes from assistants and agents. It meets the same templates, gates and approvals as human work.
Point at a source to see what it feeds, or at a result to see where it comes from.
AI on the platform
What an AI-native platform adds on top of a self-service platform.
Who does what in an AI-native SDLC
Agents draft; engineers decide. The platform enforces the same rules for both.
- Set scope and priorities
- Approve the architecture
- Architect agent drafts design notes in Git
- Reads tickets through MCP
- Repository from a golden path
- Own the code
- Backend and test agents write code and tests
- Tests, scans and quality gates
- Decide what merges
- Code-review agent on every pull request
- Policy as code blocks non-compliant change
- Approve production
- Generate pipeline, SLO and alert config for review
- One-click promotion with approvals
Agents draft code, tests and configuration. Engineers review, approve and own every change that reaches production, and the platform records who approved what.
AI on the platform: questions
The questions this part raises, and their answers.
No. The platform makes their output safe to ship at scale: the same templates, scans, policies and approvals apply whoever wrote the change.