Platform engineering for AI agents: what changes when agents become platform users

Platform engineering for AI agents adapts internal developer platforms for autonomous systems that can rapidly modify code, run tests, provision environments, and use internal APIs but lack human judgment and accountability. It emphasizes per-run identities, approved “golden path” workflows, isolated sandboxes, human approval gates for irreversible actions, production-like test environments, immutable audit logs, and cost attribution. Citing DORA research, the discussion argues that AI improves organizational performance primarily when platform quality is high, while fragmented agent tools, weak controls, unclear value, and rising costs can undermine projects. Agents differ from human developers in their speed, limited context, reliance on instructions, and ability to repeat mistakes at scale, making platform-enforced controls essential. Organizations can begin by inventorying agents, replacing shared tokens, publishing agent-readable instructions and MCP-enabled approved actions, adding gates, logging runs, and measuring delivery and cost outcomes. Upsun Dispatch is presented as a workflow governance product with sandboxes, gates, and audit trails, while Upsun Cloud provides infrastructure-as-code and production-like preview environments for testing agent-generated changes before human approval.

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