AI is getting better at doing the work that comes after a recommendation. Instead of telling someone what action to take, an AI agent can increasingly take that action itself.
Recent cybersecurity testing from OpenAI and Anthropic shows why that distinction matters. During internal testing, unreleased AI models gained unauthorized access to outside companies, including Hugging Face. The incidents happened without direct human involvement at the time of the breaches, raising complicated questions about who is responsible when an autonomous system goes beyond what its developers intended.
The legal questions may take years to sort out. But companies adopting AI agents have decisions to make much sooner, especially as those systems gain access to budgets, customer information, publishing tools, and other parts of the business.
Marketing Agents Can Do Real Work
The difference between an AI assistant and an AI agent becomes easier to see in a marketing campaign. An assistant might recommend moving more budget toward a high-performing channel. An agent connected to the advertising platform could make the change.
The same applies across the campaign. An agent could adjust targeting based on performance, identify creators who match an audience, schedule content, respond to customer questions, or pull information from audience and customer data.
Each action reduces the amount of manual work required from the team. It also gives the system more influence over what customers see, where company money goes, and how the brand presents itself.
Give Autonomy a Range
Human oversight doesn't require a person to approve every small decision. If someone has to confirm each action, much of the value of an autonomous system disappears.
A company could instead define the range where an agent is allowed to operate. A media agent might move budget between approved campaigns as long as total spend stays the same. Increasing the overall campaign budget by 20 percent could still require approval.
Creator selection could work similarly. An agent might analyze an audience, compare potential creators, and narrow hundreds of options into a qualified group. A person could remain responsible for reviewing the final selections before outreach begins.
The important part is deciding those boundaries before the agent starts acting.
Teams should know which decisions can happen automatically and which ones are important enough to require human approval.
Access Should Match the Task
The same boundaries apply to information. Marketing teams work with customer records, campaign performance, audience research, creator data, budgets, competitive information, and internal strategy. Connecting an AI agent to every available source may make it more capable, but it also gives the system access it may not need.
An agent optimizing paid media might need campaign costs, performance data, and approved audience information. It probably doesn't need access to every customer record or internal company document.
A creator-selection agent may need audience behavior, creator performance, brand requirements, and campaign goals. That doesn't mean it should also have permission to publish content or change the campaign budget.
Brand Safety Becomes Operational
Marketers already think about brand safety when deciding where ads appear, which creators represent a company, and what messages go into the market. AI agents add another layer because some of those decisions can happen faster and with less direct involvement from a person.
We can already see that tension in the audiences evaluating these systems. A recent RAD Intel analysis of professionals engaging with Salesforce identified one group of senior marketing and revenue leaders actively evaluating AI agents for greater productivity and faster decision-making. At the same time, they showed strong interest in human oversight, governance controls, and maintaining executive visibility over critical decisions.
The pattern also appeared among marketing operations professionals. They were interested in automating repetitive campaign work and increasing campaign speed, while still prioritizing permissions, approval paths, and operational control. Both groups want AI to do more. They also want clearer boundaries around what it can do independently.
Those boundaries will look different depending on the task. An agent might identify a strong creator match, while the company still decides whether contacting that creator requires human approval. Campaign data might justify moving budget, while an approved spending limit determines how far the agent can go.
We're working through these questions at RAD Intel as agents begin supporting more work across the company. Part of that process is identifying which tasks agents can handle independently, where human review still matters, and who remains responsible for the outcome.
Someone Still Owns the Decision
As AI agents become more capable, companies will have to get more specific about what those systems are allowed to do. That includes what they can access, what they can change, how much money they can move, what they can publish, and when they need permission to continue.
The legal system may still be working through who is responsible when an autonomous AI system causes harm. Businesses don't need to wait for a court decision to decide who is responsible for an AI agent inside their own organization.
For marketers, that starts with treating autonomy like any other permission given to someone acting on behalf of a brand. Give the system enough access to do its job, define where that authority ends, and make sure someone knows when to step in.
