Decide how much autonomy to give an AI agent

Autonomy is a spectrum

Rule-based workflow automation makes no real decisions. Partially autonomous agents plan and act but keep a human in the loop for high-stakes steps, and the n8n guide says most production deployments sit there. Fully autonomous agents operate with broad independence, with people intervening only on exceptions.

Before building, place the task on that line: which steps need approval from a person, and which can run unattended?

Why one bad step is expensive

When an agent acts across tools, data stores and other agents, a single bad inference can cascade. A misread instruction becomes a wrong action, which can corrupt records in other systems, and multi-agent setups widen that blast radius.

The same guide notes that modern agentic systems can have direct access to the system they run on, which it treats as the highest-risk arrangement.

A short pre-flight list

Give the agent the lowest level of independence that finishes the job.

Keep a human approval on the steps you would not hand to a new hire unsupervised.

Limit what it can reach, including accounts, records and actions, before you spend time improving its prompts.

Borrow a threat model rather than inventing one. The OWASP Agentic Security Initiative publishes Agentic AI - Threats and Mitigations, described on the OWASP site as the first in a series giving a threat-model-based reference of emerging agentic threats and mitigations.

If you need a documented risk process rather than a checklist, the US National Institute of Standards and Technology publishes the AI Risk Management Framework (AI RMF 1.0) with a Playbook, a Roadmap and a Crosswalk, and notes on the same page that AI RMF 1.0 is being revised under the White House AI Action Plan.