Platform engineering in the age of AI | Harness Blog

A Harness blog post based on an InfoQ panel with contributors from Harness, DKB, and Shine examines how platform engineering is adapting as AI becomes both a development aid and an active consumer of platform services. It highlights a reported finding that 94% of engineering leaders lack the AI metrics they consider most important, particularly measures connecting AI-generated code, agent activity, token spending, productivity, infrastructure costs, and business outcomes. Panelists describe a growing need for platform teams to determine which AI tools and controls should be standardized centrally versus selected by individual teams, while also redesigning internal developer platforms and APIs for AI agents that may autonomously request infrastructure or deploy software. Although AI has accelerated coding, slower stages of the software development lifecycle, including governance, testing, and deployment, can become bottlenecks or introduce production risks when bypassed. The post argues that platform engineering is not being replaced but is taking on expanded responsibilities for governing AI tooling, enabling secure agent self-service, and demonstrating the value of AI investments.

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