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Agentic Automation

Agentic Automation Needs an Operating Model

The decisions, permissions and operating responsibilities that make AI agents useful in daily work.

Agentic Automation Needs an Operating Model

Define Responsibility Alongside Capability

Agentic automation changes the nature of workflow automation because the system can interpret context, choose tools, plan steps, and recover from exceptions. That capability is powerful, but it also raises the bar for governance and operating design.

What the Operating Model Must Define

  • Which actions the agent can take without approval
  • Which systems and records it can access
  • When it should escalate to a human
  • How failures, retries, and overrides are logged
  • Which metrics determine whether the workflow is improving

Design for Real Conditions

Production workflows include missing data, conflicting instructions, permission gaps, angry customers, edge cases, and process exceptions. Agentic automation has to be designed for those conditions from the start.

Measure completed work alongside quality, intervention rate and recovery time. An agent that handles routine cases well must also make exceptions visible and leave an accountable person able to intervene.

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Written by

Ranjit Rajput

Founder, RDMI

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Agentic Automation Needs an Operating Model | RDMI Blog