AI agents can now do far more than summarise information or carry out individual tasks. They can retrieve data from different systems, identify connections, compare options, and use that information to prepare actions or even execute them independently.
That is where their potential lies, but also a central challenge. Just because an agent can make a decision does not automatically mean it should.
The key question in an enterprise setting is therefore not only:
"What can an AI agent automate?" but "Where does the agent's responsibility end, and where must a human take over?"
In many AI projects, this boundary is still not defined deliberately enough.
Between automation and responsibility
An AI agent fundamentally works on the basis of information, rules, context, and defined goals. Depending on the process, it can determine the next steps independently.
For a simple task, that is often unproblematic. An agent could, for example:
- Coordinate an appointment according to defined rules
- Categorise incoming enquiries
- Combine data from multiple systems
- Create a traffic report
- Automatically trigger a recurring process
In such cases, full automation can make sense. The impact of a wrong decision is limited and can often be corrected easily. The situation changes when a decision has far-reaching consequences.
What happens, for example, if an agent:
- Rejects an important customer enquiry,
- Approves an order,
- Grants a discount,
- Changes a contract,
- Initiates a payment, or
- Makes a business-critical decision based on incomplete data?
Here, it is not enough for the agent to be technically capable of doing so. It must be defined whether it should be authorised to do so.
Not every task needs the same level of autonomy
A common mistake when introducing AI agents is to treat automation as a binary decision: either the agent handles a process entirely or a human handles it entirely.
In practice, there are numerous possibilities in between. A sensible approach is to define different levels of autonomy.
1. The agent informs
The agent collects information and makes it available to an employee.
For example, it analyses a customer enquiry, searches relevant data, and summarises the most important information.
The decision remains entirely with the human.
2. The agent recommends
The agent goes one step further and proposes a concrete action based on its analysis.
A sales agent could, for example, recommend a discount because certain criteria are met.
The human reviews and confirms.
3. The agent prepares
The agent carries out all preparatory steps independently but needs approval before the final action.
For example, it drafts a contract, prepares an order, or drafts a response to an important customer.
The human approves the final action.
4. The agent acts autonomously
For clearly defined, low-risk processes, the agent can also execute the final action itself.
For example, it can start an internal workflow or automatically handle a standard enquiry.
The human intervenes only when an exception occurs.
These levels enable companies to benefit from agents without automatically delegating every decision.
The right boundary depends on the risk
The question of appropriate autonomy cannot be answered in the same way for every process. One decisive factor is the consequences a wrong decision would have.
A simple internal task could, for example, be fully automated, while a customer-related or financial decision requires additional approval.
Companies can consider several factors:
Financial impact: The greater the potential financial risk, the more important human approval can be.
Legal impact: Decisions with legal consequences should be controlled particularly carefully.
Customer relevance: Automatically sending standard information is different from deciding on a complaint or an important customer case.
Reversibility: Can a decision be easily reversed? The more difficult an action is to correct, the more cautiously automation should be designed.
Data quality: An agent can only act as reliably as the available context allows. If data is incomplete, contradictory, or outdated, the process may need to be handed over to a human.
Human-in-the-loop is more than an approval button
The term human-in-the-loop is often reduced to a simple approval step: the agent works, and an employee then clicks "Confirm".
That falls short.
A meaningful human-in-the-loop process defines much more precisely when, why, and under what conditions a human must intervene.
For example: if the order value exceeds a defined threshold, the action requires human approval.
Or: if the available customer data is insufficient, the agent must not make a decision and has to hand the case over to an employee.
Or: if a decision deviates from the standard process, a human review is triggered automatically.
Human control thereby becomes part of the process rather than a safety measure added afterwards.
What happens when the agent is uncertain?
Another important point is often underestimated in agent systems: a good agent does not always have to provide an answer. Sometimes the right action is not to decide independently.
If information is missing, data is contradictory, or a situation falls outside the defined scope, the agent should be able to recognise that its options are limited.
This can be implemented through escalation paths. In simplified form, the process could look like this:
Analyse data → Prepare a decision → Assess risk → Act autonomously or escalate
The agent is thereby given not only the ability to act but also clearly defined boundaries.
This is particularly important when agents are connected to multiple enterprise systems. The more tools, data sources, and actions are available, the greater the potential scope of action becomes.
Autonomy should be designed deliberately
When developing an AI agent, it should therefore not only be defined which tasks it is allowed to carry out.
It is at least as important to define what it must not decide independently.
| Decision | Risk | Autonomy |
|---|---|---|
| Summarise internal information | Low | Fully automatic |
| Answer a standard enquiry | Low | Automatic |
| Suggest a discount | Medium | Recommendation |
| Approve a discount above a threshold | High | Human-in-the-loop |
| Make a legally relevant decision | Very high | Human decision |
The specific boundaries must, of course, fit the individual company and process. The principle remains the same: the higher the risk, the stronger human control should be.
The agent needs better rules, not fewer
The goal of an agent process should therefore not be maximum autonomy at any cost. A good agent is not necessarily the one that makes as many decisions as possible.
A good agent is the one that knows exactly which decisions it may make itself and when it must involve a human.
This requires clear rules for:
- Permitted actions,
- Decision boundaries,
- Required approvals,
- Escalation cases,
- Data access,
- Responsibilities, and
- Logging.
This turns a technically functioning agent into a controllable business process.
The key question is not "How much can we automate?"
AI agents give companies the opportunity to automate processes much more extensively than before. At the same time, this shifts responsibility for process design.
Companies must not only consider which tasks an agent can take on. They must define which decisions an agent is allowed to make.
There is a crucial difference between "The agent can do it" and "The agent should do it". Particularly in production enterprise AI applications, the right balance is therefore essential:
Agents do the work; humans retain control where it is needed.
The goal is not maximum autonomy. The goal is appropriate autonomy.