From AI Assistance to Execution: Staffing an Agentic Workflow
Updated: 3 days ago
Moving from suggested actions to executed actions changes the skills your team needs. A workflow that writes to a business system must manage permissions, failures, and incomplete information. The hiring brief should reflect that responsibility.
Separate reasoning from execution
Ask candidates how they would distinguish model-generated plans from allowed actions. A well-designed workflow restricts tool access and checks inputs before changing records. The system should have explicit limits rather than relying on a prompt alone.
Assess integration reliability
Look for evidence of building dependable APIs and background workflows. Discuss retries, duplicate prevention, partial completion, and monitoring. An engineer should explain what happens when the model succeeds but an external service fails halfway through the task.
Keep a clear escalation path
Assign operational ownership and define which cases return to a person. Product and process leaders should set the acceptance criteria, while engineering teams implement and test the controls. Start with a narrow scope before expanding autonomy.
Hiring checklist
Specify which systems the agent may change.
Define irreversible or high-impact actions requiring review.
Test partial failures and repeated requests.
Document operational ownership and escalation.
Consider an agent updating a CRM and creating a follow-up task. If the CRM update succeeds and task creation fails, the engineer needs a recovery design that avoids duplicating the first action.
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