Connecting AI Agents Is Easy. Governing Them Is the Hard Part.

Enterprises often rely on fragmented automation tools across infrastructure, data, applications, and business processes, creating gaps in observability, governance, and incident response when workflows span domains. The piece argues that a unified orchestration layer can coordinate these systems end to end, particularly as AI agents are introduced to perform tasks within and across domains. Although connecting agents through APIs and MCP may be straightforward, effective deployment requires controls over data access, permitted actions, compliance, human approvals for critical decisions, and a persistent transaction identifier outside the agent to preserve auditability. It presents Kestra’s execution model as an example of unifying AI steps, human decisions, and remediation within one governed workflow, while supporting multiple AI models and deployment environments. The discussion also emphasizes designing for data sovereignty, privacy, security, hybrid infrastructure, and air-gapped environments from the outset, and cites JPMorgan Chase’s use of Kestra to collect, analyze, and distribute threat intelligence from external feeds in near real time.

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