AI agents do not just need model access. They need reliable, well-governed APIs that expose capabilities safely, predictably, and with the right operational guardrails. In practice, most organizations are still building AI on shared infrastructure where isolation, quota management, observability, rollout safety, and performance trade-offs matter as much as inference quality. In this session, I will share lessons from designing multi-tenant AI platforms on Red Hat OpenShift AI with NVIDIA MIG-based GPU partitioning, workload orchestration, and platform controls in regulated enterprise environments.

The talk will focus on the API and platform patterns that make AI systems consumable by internal teams, applications, and increasingly by autonomous agents. I will cover common failure modes, including weak tenancy boundaries, poor workload scheduling, brittle upgrades, and APIs that expose models without sufficient policy, telemetry, or cost controls. I will then show a practical blueprint for designing agent-ready platform APIs: clear service boundaries, policy enforcement, quota-aware interfaces, traceable execution paths, and day-2 operational playbooks. Attendees will leave with a concrete checklist to evaluate whether their APIs and underlying platform are actually ready for machine-driven consumption, not just human developers.

The goal is to help teams move from isolated AI experiments to secure, reusable, production-grade platform.