The CNCF Platform Engineering Maturity Model defines four levels: Provisional, Operational, Scalable, and Optimizing. Each level applies to both human and AI platform consumers. The table below shows the human level (top row) and AI agent equivalent (bottom row) for each dimension.
| Dimension | Provisional | Operational | Scalable | Optimizing | |
|---|---|---|---|---|---|
| Investment | Human | Voluntary, temporary | Dedicated team | Platform as product | Enabled ecosystem |
| AI | Ad-hoc API keys, manual setup | Dedicated agent infra, budgeted tokens | Agent platform as product, ROI per task | Self-provisioning agent ecosystem | |
| Adoption | Human | Erratic | Extrinsic push | Intrinsic pull | Participatory |
| AI | Hardcoded tool lists | Tool registries, skill loading | Self-discovery, capability advertisement | Agents contribute tools back | |
| Interfaces | Human | Custom processes | Standard tooling | Self-service | Integrated services |
| AI | Raw CLI, unstructured prompts | MCP tools, structured schemas | Self-service composition, skill chaining | Multi-agent orchestration | |
| Operations | Human | By request | Centrally tracked | Centrally enabled | Managed services |
| AI | One-off scripts | Centrally managed skills | Automated curation, health checks | Agents maintain own platform | |
| Measurement | Human | Ad hoc | Consistent collection | Insights | Quantitative and qualitative |
| AI | Manual log review | Success/failure tracking | Automated pattern detection | Agents optimize agents | |
| Context | AI | Entire files in prompt | Curated system prompts | Dynamic context injection | Agents request what they need |
| Memory | AI | No persistence | Session annotations | Structured long-term memory | Automatic extraction, federated knowledge |
| Authority | AI | No boundaries | Static allowlists | Delegated authority with escalation | Dynamic trust based on track record |