Practical insights on Platform Engineering, Azure, GitHub, Terraform, and AI-powered delivery
AI-assisted engineering is moving beyond “can the agent do the task?” and into the same practical concerns platform teams already deal with: cost visibility, ownership, observability, governance, repeatable workflows, and sensible defaults.
This post looks at why AI engineering needs boring platform controls, from APIM policies and token visibility through to Agent Skills, MCP, Terraform modules, OpenTelemetry, and evaluation loops.
AI-assisted engineering is moving beyond proving that agents can complete tasks. The harder question is whether those workflows can be repeated, reviewed safely, kept within sensible cost, and improved over time.
This post looks at why platform teams need to treat AI usage as more than activity metrics, with a focus on token visibility, repeatable agent skills, controlled context, practical guardrails, and measurement that connects AI-assisted work to real engineering outcomes.