5 governance questions to answer before you build your AI agent

Celigo's five-question governance model applies to every internal AI use case — data access, allowed actions, data handling, ownership and control, audit and oversight. Running all five through one orchestration layer means IT approves the model instead of approving every agent. What you'll learn: ▸ The five questions any AI use case has to answer before it ships, and why answering them at the platform level means answering them once instead of per agent ▸ Why "human in the lead" is a higher bar than human in the loop, and what changes when one named person is accountable for what an agent does ▸ What a context layer does that a prompt typed straight into ChatGPT or Claude cannot — guardrails, added context, and a record of both ▸ Why a token-spend report is not an audit trail, and what real oversight looks like when every prompt and response passes through an integration layer ▸ How audit data turns into an improvement loop ▸ How to let an agent earn autonomy in stair steps instead of granting it on day one CHAPTERS 00:00 The governance model: five questions every AI use case has to answer 00:36 Data access: one source of truth, solved before anyone builds 00:56 Allowed actions: what AI can touch, and who is responsible for it 01:15 Data handling: why a context layer beats a raw prompt into an LLM 01:46 Ownership and control: human in the lead, not just human in the loop 02:19 Audit and oversight: a token report is not an audit trail 03:05 Repeatability is what separates a one-off prompt from an enterprise agent 05:04 Letting agents earn autonomy in stair steps Answer the five questions once at the platform level, and autonomy becomes something an agent earns at scale. 🚀 Never miss Celigo Sessions: https://www.celigo.com/webinar_series/celigo-sessions 👉 Get a demo: https://www.celigo.com/request-a-demo/ #Celigo #AIGovernance #EnterpriseAI #AIAgents #iPaaS #IntegrationPlatform #HumanInTheLoop #ITLeadership