Establish SLOs/SLAs for inference services and build operational excellence (load testing, capacity planning, incident response playbooks, regression detection, and continuous performance benchmarking); build robust performance and cost observability (latency histograms, token throughput, GPU utilization, memory fragmentation, cache hit rates, per-tenant cost attribution) and automate remediation of recurring issues. Architect and govern agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.