AI Agents · Practical

Approvals in Agentic Workflows: Step-Up Controls for High-Risk Actions

Amestris — Boutique AI & Technology Consultancy

As soon as an AI system can take actions—send emails, update records, issue refunds—it becomes an operational risk surface. Most incidents occur not because the model was malicious, but because it was confidently wrong.

Approvals are one of the simplest ways to bound that risk. Design approvals that are effective without turning every workflow into bureaucracy.

Enforce approvals in the policy layer

Approvals must be enforced outside the model. The policy layer should require an approval token or human signature before executing high-risk tools (see tool authorisation).

Record who approved what, when, and why—including model/prompt/tool versions (see lineage).

Quick answers

What does this article cover?

Patterns for approvals in agentic workflows: step-up confirmations, two-person rules, and auditable action trails.

Who is this for?

Teams building tool-enabled agents who need to reduce blast radius and prevent unintended actions while keeping workflows efficient.

If this topic is relevant to an initiative you are considering, Amestris can provide independent advice or architecture support. Contact hello@amestris.com.au.