Agentic AI combines a model with instructions, tools, context and a workflow so it can take bounded actions—not simply generate text.

Key points

  • Tools determine what the agent can actually do in the real system
  • Permissions and approval gates define safe boundaries for side effects
  • Observability and evaluation reveal failures that fluent output may hide

Illustrative example

Illustrative example — for teaching clarity, not a SkillsNCR outcome claim.

Compare three responses to “Update the customer's shipping address” (illustrative):

1. Text-only model: explains how an address update might work. No system change. Low risk, no verification of current data.
2. Tool-enabled lookup: agent calls get_customer(id) (read-only), returns the current address and asks which fields to change. Useful, still no write.
3. Proposed action with approval: agent drafts update_address(...) with old vs new values, requires a human approve click, logs tool call, denial or success, and rolls back guidance if the API errors.

Permissions: read customer allowed; write address only after approval; delete never granted.
Errors: if update_address returns 409 conflict, the agent reports the conflict instead of inventing success.
Human review: the approval step is the control—not a decorative checkbox after the write already happened.

How WorkReady practises this

Associates design small workflows with tool calls, error handling, human approval and an audit-friendly decision trail—not demos that only show a happy path.

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