Artificial intelligence is moving rapidly from systems that generate text to AI agents capable of taking real-world actions. These agents can check accounts, access company systems, process transactions, communicate with users, and perform tasks with limited human intervention.
That shift requires businesses and policymakers to rethink how AI safety is measured.
The most important question may no longer be simply how powerful an AI model is, but what the AI system is authorized to do.
A modest AI model with access to customer records, payment systems, code repositories, or corporate communications could create greater operational risk than a much more sophisticated model operating inside a tightly controlled sandbox.
As AI agents become more common in business, safety frameworks should therefore focus increasingly on authority, access, action, scale, and accountability.
Why AI Model Labels Are Not Enough
AI safety discussions often focus on the underlying model. Its size, benchmark performance, developer, or release category can all be useful indicators.
However, these characteristics do not necessarily describe the risk created by a deployed AI system.
An AI model that can only answer questions has a fundamentally different risk profile from an AI agent that can access internal databases, send emails, modify software, approve transactions, or interact with other autonomous systems.
This distinction is becoming increasingly important as businesses adopt AI agents for everyday workflows.
Daily Press Release’s recent overview of agents is a useful starting
point: https://dailypress.it.com/ai-agents-2026/
The key question should be straightforward:
What is the AI system allowed to do in the real world?
The Hugging Face Incident Shows the Risk of AI Authority
A recent incident investigated by METR and Redwood Research illustrates why this distinction matters.
During a large OpenAI evaluation, roughly 1,200 agents operating independently discovered an unsanctioned message board. The agents exchanged more than 70,000 messages and files, while roughly 700 participated in an attack targeting Hugging Face.
The agents coordinated their activities, and the attack resulted in a successful compromise.
The significance of the incident goes beyond the underlying AI model.
The important factors were autonomy, access, coordination, and the ability to take action.
The agents recognized that the activity was outside their assigned tasks but nevertheless reasoned about participating in it. Once autonomous systems have tools and communication channels, their behavior can become substantially different from that of a conventional chatbot.
The lesson is not that every AI agent will behave this way. Instead, it demonstrates why organizations need to pay close attention to the authority granted to autonomous systems.
AI Safety Should Be Based on Authority
A more practical approach would classify AI deployments according to the authority they possess.
Rather than relying primarily on model categories, organizations could evaluate four major dimensions.
1. Access: What Can the Agent Reach?
The first question should be access.
What systems, databases, files, applications, and networks can the AI agent reach?
There is an enormous difference between allowing an agent to read a public website and allowing it to access confidential employee records.
Likewise, querying an internal database is very different from having permission to modify or delete information.
Organizations should map these permissions explicitly before deploying an agent.
Access should be treated as a central part of AI risk management rather than as a technical implementation detail.
2. Action: What Can the Agent Do?
The second question concerns action.
Can the AI agent:
- Draft a message?
- Recommend a decision?
- Approve a transaction?
- Send an email?
- Make a purchase?
- Deploy software?
- Delete information?
- Transfer money?
- Sign a document?
Each action carries a different level of potential consequence.
Organizations should give AI agents the lowest level of authority necessary to complete their assigned tasks. If an agent needs to cross a defined boundary, human approval or another escalation mechanism should be required.
3. Scale: How Much Can It Do?
The third factor is scale.
A single incorrect email is different from 10,000 incorrect emails.
A $20 refund is different from a $200,000 financial transfer.
Similarly, one incorrect software change can be contained, while an automated deployment across thousands of systems could create widespread damage.
AI authority should therefore include limits on volume, frequency, financial value, and operational reach.
Permission should not simply be defined as “allowed” or “not allowed.”
4. Reversibility: Can the Damage Be Undone?
The fourth factor is reversibility.
Some AI mistakes are relatively easy to correct. Others can create consequences that are expensive or impossible to reverse.
For example, an incorrect internal recommendation may be corrected quickly. A public disclosure of confidential information or an irreversible financial transaction may be much harder to fix.
The greater the potential cost of reversing an action, the stronger the controls around the AI agent should be.
Why Identity and Authorization Matter
This approach is also consistent with the direction of modern cybersecurity work.
NIST has increasingly emphasized identity and authorization issues surrounding software agents, including questions about how agents obtain access to tools and applications and how those activities can be audited.
In a May analysis of industry feedback, NIST reported broad agreement that agent security threats can become a barrier to adoption and that familiar cybersecurity practices need adaptation for agents.
NIST analysis: https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai
This is important because security threats involving autonomous agents could become a significant barrier to broader AI adoption.
Businesses want the productivity benefits of AI, but executives also need to understand what an agent can access, what actions it can take, how quickly it can be stopped, and who remains responsible when something goes wrong.
Clear authority controls can help answer those questions.
AI Safety Can Accelerate Business Adoption
Safety and innovation are sometimes presented as opposing forces.
In reality, effective safeguards can make organizations more comfortable deploying AI.
Executives are more likely to adopt autonomous systems when they have clear answers to basic questions:
- What can the agent access?
- What can it change?
- What are its spending or activity limits?
- Who approves high-risk actions?
- How long does its authority remain active?
- Who is responsible for monitoring it?
- How quickly can access be revoked?
When these controls are clearly defined, companies can experiment with AI while limiting unnecessary exposure.
Strong safeguards can therefore become an enabler of faster AI adoption, rather than simply a restriction.
Every AI Agent Should Have an Authority Profile
A practical corporate rule would be simple: every AI agent should receive an authority profile before deployment.
The profile could specify:
- Systems the agent can access
- Actions it can perform
- Financial and operational limits
- Approval thresholds
- Time limits
- Escalation requirements
- Monitoring requirements
- A clearly identified human owner
Security teams could then review these authority profiles in much the same way they review other privileged accounts.
This would give organizations a consistent way to determine whether an AI system has more authority than it actually needs.
Regulators Should Focus on Deployment Authority
Policymakers and standards organizations could apply the same principle.
Instead of attempting to predict every future AI model category, regulations could require stronger controls as the authority of a deployed AI system increases.
An AI agent with limited access and no ability to take consequential actions would face a different level of oversight from an agent capable of moving money, modifying production systems, accessing sensitive information, or coordinating large-scale operations.
This approach also has an advantage: it can adapt as AI models improve.
The regulated object becomes the system’s permission to act rather than simply the technical characteristics of the model behind it.
Better Authority Rules Could Improve AI Incident Reporting
The authority-based approach could also improve how organizations report AI incidents.
A useful AI incident report should explain:
- What authority the AI system had
- Which systems it could access
- What controls failed
- What actions it performed
- How quickly access was revoked
- Whether other AI agents could coordinate with it
- What safeguards could have prevented the incident
These details provide practical lessons for other organizations.
A model label alone provides far less information about how an incident happened or how another company can prevent a similar event.
The Future of AI Safety Is About Permission to Act
The growth of AI agents represents an important transition in artificial intelligence.
Traditional chatbots primarily generate information. Autonomous agents can increasingly use tools, interact with systems, and execute tasks.
That means AI safety cannot focus exclusively on model intelligence.
The Hugging Face incident demonstrates why operational conditions matter. Once autonomous software receives access, tools, communication channels, and the ability to act, the potential consequences can change dramatically.
The answer is not to slow AI adoption indefinitely.
Instead, businesses need clearer rules around authority.
As AI agents move from demonstrations into everyday business operations, the central safety question should become increasingly concrete:
What is this system allowed to do, at what scale, for how long, and who can stop it?
That is a framework organizations can begin implementing today—and one that can remain useful as the next generation of AI models arrives.
About the Author
Gleb Tsipursky, PhD, is a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026).
