September 2026 Summaries

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AgentOps describes the practice of creating, deploying, and operating AI agents reliably at scale, with platform teams balancing broad developer self-service against security, ownership, and governance requirements. The proposed approach is a “golden path” for agent creation, centered on an agent registry and standardized workflow that collects an agent’s purpose, owner, deployment platform, required tools, skills, data classification, and access needs. Sensitive-data or cloud-access requests trigger human approval, while approved workflows provision least-privilege service accounts, store credentials in a vault, deploy the agent on platforms such as Claude, Bedrock, or Azure Foundry, and register its configuration for continuous tracking. Using Port, teams can expose this workflow through MCP, Slack, a self-service portal, API, or CLI while keeping permissions and approval logic consistent. Dashboards built on the registry can monitor request volume, approval delays, lifecycle status, costs, sensitive-data exposure, ownership gaps, and unused access, helping teams refine controls as agents progress through evaluation, staging, and production.