Treat agent output as a candidate change
Claude Code, Codex and similar tools can reason across large repositories and execute tests. Their output should still enter the same delivery controls as human-authored code. The goal is not distrust; it is repeatability.
A useful control plane records what source was analyzed, what changed, which artifact was built, which checks passed and which release is currently live.
A complete path from code to runtime
The delivery loop should be visible as a sequence rather than buried in one long job.
- Understand the repository and workload shape
- Scan security, performance and architectural readiness
- Apply and verify remediation
- Build an immutable release artifact
- Promote through isolated environments
- Verify health, retain evidence and preserve rollback
Why a scanner or PaaS alone is not enough
A scanner can identify problems but usually stops before the fixed application is running. A PaaS can run an application but may know little about the code quality that entered the build. CI scripts can connect the two, but the operational state is scattered.
AnchorScape combines these concerns in one product model: code understanding, remediation, environments, multi-service deployment, release records, domains, export bundles and operational tooling.
Use the same platform on your own products
Infinovation uses AnchorScape to operate its applications. That creates the feedback loop we care about: deployment features are exercised against real multi-service AI systems, not only sample repositories. AnchorScape remains a private pilot, and access is reviewed by request while the operating model is hardened.