Articles / The wrong-action problem: why agentic AI needs a different kind of governance
By Gianluca Busato · Founder, CEO & AI Systems Architect · 2026-06-17
By Gianluca Busato — Founder, CEO & AI Systems Architect, Enkronos
For most of the AI era, the unit of risk was a wrong answer. A model returned text; a person read it and decided what to do. If the answer was wrong, a human caught it before anything happened. The blast radius of a mistake was one bad sentence.
Agentic AI changes the unit of risk to a wrong action. Autonomous agents now plan, call tools, hit APIs and trigger real effects — moving money, editing records, sending messages, provisioning infrastructure — often with no human reading anything in between. The blast radius of a mistake is no longer a sentence. It's whatever the agent was able to touch.
This is a qualitative shift, and it breaks the assumptions most AI safety work was built on.
A lot of effort goes into making models more accurate. That helps with wrong answers. It does almost nothing for wrong actions, because the failure mode isn't only "the model was wrong" — it's "the model was right and still did something it shouldn't have been allowed to do."
An agent can correctly reason that the fastest way to resolve a ticket is to delete a database. Accuracy isn't the problem there. Authority is. The question that matters for agentic AI is not "is this output correct?" but "was this action authorized, is it auditable, and is it reversible?"
Containing wrong actions takes defense in depth — independent layers, each of which re-checks rather than trusting the one before it:
verifiable identity.
deterministically.
action before it happens; high-impact ones require human approval.
reconstructable.
The power is in the independence. A compromised or misbehaving agent is contained because each layer checks authority on its own. One failure doesn't cascade.
Two ideas borrowed from security engineering matter enormously here:
bounded. An agent that can do anything is a liability no matter how smart it is.
behind stronger checks.
These aren't constraints on capability. They're what let you grant capability safely.
The organizations that win with agentic AI won't be the ones running the most agents. They'll be the ones that can trust the agents they run. And trust here isn't a feeling; it's engineered — at the architecture level — through identity, deterministic policy, mediated execution and immutable audit.
Get those right and autonomous AI stops being a gamble and becomes infrastructure.
This is the problem AINOVA's Multi-Layer Governance Architecture is built to solve. More at gianlucabusato.com.