The blog post by Julia Berman makes a sharp distinction between shadow AI and shadow AI agents — and argues the latter is far more dangerous. I agree with her.
For many enterprises, AI risk still centers on unsanctioned prompts and shadow tools. But the next wave of risk is already emerging, driven by autonomous agents that can take action across systems with limited human oversight.
When connected to enterprise tools and systems, AI agents can monitor signals, trigger workflows, update records, and send messages, sometimes with minimal human input. This means the risk is no longer “shadow AI” in the traditional sense (i.e., GenAI use outside of sanctioned tools). The distinction is that AI agents can act with little or no human input. The bigger risk, then, is what these agents are doing and whether anyone has visibility into it.
From Shadow AI to Shadow Agents
Shadow AI is a usage problem — unsanctioned tools and untracked prompts. Shadow agents are different because they act autonomously: triggering workflows, updating records, and making decisions with minimal human input. The “shadow” part isn’t about autonomy itself — it’s the visibility gap. These agents are unregistered, unmanaged, or unobservable inside the organization’s governance model.
A shadow agent often starts small — auto-triaging tickets or monitoring thresholds. Over time it accretes responsibility: a triage rule quietly becomes a decision heuristic, a threshold becomes de facto policy. Eventually the agent is load-bearing but no longer fully understood.
Once agents quietly become part of the organization’s operational fabric even without being formally reviewed, monitored, or governed, the risk equation changes. Shadow agents turn experiments into unofficial operations.
The Hidden Cost of Autonomy: Automation Debt
Like technical debt, automation debt accumulates silently. It builds every time:
- An agent’s logic changes without traceability
- A prompt is updated without review
- An agent triggers an action and no one can later explain why
The debt isn’t about brittle logic — it’s about broken lineage. When something goes wrong, leaders are left asking the questions governance was meant to answer: Who approved this agent? What model was it using? What data did it access? Was a human ever involved?
Without oversight, these questions materialize as consequences instead of answers. Automation debt becomes harder and more expensive to unwind the longer it remains invisible.
Why Traditional Governance Breaks Down
Most governance frameworks were designed for a different era of system development. One where systems were relatively static, deployments were deliberate, and behavior was easier to predict based on testing.
AI agents break all three assumptions — they evolve continuously and chain actions across systems. Approval-once, deploy-once governance doesn’t work when risks emerge from how systems behave over time.
Visibility Is the Missing Primitive
The organizational instinct is to add more rules, approvals, and documentation. But those don’t address the core problem. The article uses an aviation analogy: control towers don’t fly planes — they provide situational awareness. Organizations need the same for AI agents: a central view of which agents exist, what they’re connected to, what data they touch, and how they behave over time.
Human-in-the-Loop as a Design Choice
Human-in-the-loop isn’t about slowing things down — it’s about deciding where accountability lives. Not every action needs review, but every agent needs clear escalation paths and defined evaluation points. Oversight should be a feature of the system, not an afterthought.
The Litmus Test
A useful litmus test for automation debt is dependency asymmetry: Can the team articulate how long the business could function if a given agent stopped working?
When that question can’t be answered confidently with a defined timeframe, risk has already shifted from theoretical to operational.
Governance shouldn’t be an afterthought; it must be woven into the foundation of agentic AI. It has to live everywhere — quietly, continuously, and in service of trust.