In recent years, much of enterprise artificial intelligence has been used to answer questions, generate documents, or retrieve information. The emergence of agents introduces a significant shift: the system no longer necessarily limits itself to providing an answer; it can interact with applications and prepare or execute specific actions.
An assistant can check the status of an order and explain it to a person. An agent, on the other hand, could detect an issue, review inventory, propose an alternative, update the management system, and generate a communication for the customer.
This shift matters because, when AI begins to intervene in a process, an error can have operational consequences. We must no longer evaluate only the quality of a response, but also what information it used, what actions it can perform, what effects those actions may have, and who remains responsible for the decision.
An advanced model does not inherently understand how a company operates.
In August 2026, SAP introduced an artificial intelligence strategy tailored by industry. Its approach is based on a key idea: even highly advanced general-purpose models are not sufficient to solve certain business decisions.
According to the company, situations such as reorganizing thousands of technicians after a storm, maintaining a production line, or responding to supply chain disruptions require data, regulation, domain knowledge, and an understanding of workflows. This reflects a broader reality in enterprise AI: the model is only one part of the system.
It is important to clarify what “understanding” means in this context. An agent does not understand a process as a human would. What it can do is operate with a sufficiently complete representation of its rules, states, participants, data, exceptions, and boundaries.
The problem arises when that representation does not match reality. A procedure may state that all requests above a certain amount require approval. In practice, exceptions may exist depending on the supplier, urgency, availability, or contractual conditions. If the agent only knows the general rule, its behavior may appear logical but still be operationally incorrect.
Six signs that a process is ready to incorporate AI agents.
Before deciding which agent to build or which model to use, there is a more basic question: is the process sufficiently prepared? Our AI Lead identifies six key signals:
1. The process exists beyond habit
A process is sufficiently defined when it is documented and two different people can explain it in a similar way.
If each department describes it differently, the first step is not automation, but aligning and structuring the process. Otherwise, the agent will inherit those inconsistencies.
2. The data can be accessed by a machine
The required information must be available through an API, a database, a data historian, or another controlled system.
If part of the context exists in emails, informal conversations, or a spreadsheet on someone’s desktop, the agent will operate with an incomplete view.
This does not mean all data must be centralized. It means it must be identified, accessible, and consistently interpreted.
3. The decision criteria can be explained
A rule such as “if flow drops below X for Y minutes, perform Z” can be translated into controllable and verifiable logic.
When decisions rely entirely on tacit human experience, a prior step is required: extracting, validating, and documenting that knowledge.
Not all expertise can be reduced to simple rules. However, the agent needs to know which conditions it can evaluate and when to escalate.
4. The volume or criticality justifies the effort
A process may be a candidate because it is repeated frequently or because, even if less frequent, its consequences are significant.
Automating a rare, simple, low-impact task may require more effort than it saves. In contrast, a less frequent but critical operation related to service continuity, safety, or compliance may justify a tailored solution.
5. The outcome can be verified
After a recommendation or action, it must be possible to determine whether it was correct.
Was the anomaly confirmed? Did the order arrive on time? Did the intervention resolve the issue? Was the recommendation accepted or modified?
If the outcome cannot be verified, the agent cannot be measured or improved.
6. There is a clear process owner
The project requires a person with the knowledge and authority to define rules and exceptions.
“Without this role, projects often stall at the first uncertainty”, explains Monzón, IA Lead at MindDen.
The technical team can build integrations and models, but should not decide how the business operates or which exceptions prevail.
The risk of automating only the easy path.
One of the most common challenges is designing agents around the most straightforward cases while excluding exceptions. In such cases, the agent may handle 70–80% of scenarios, while the rest becomes additional manual work—ultimately eroding efficiency gains.
For example, an agent that classifies support tickets may handle standard cases well, but misclassify issues involving multiple services. Teams then need to identify, correct, and reconstruct context manually.
Automation solves the simple part but concentrates complexity in the remaining cases.
This does not mean an agent must handle everything from day one. Exceptions can be escalated to humans—but they must be identified, designed for, and measured.
Automating disorder only makes it fail faster.
An agent can efficiently execute a poorly designed process—but that does not improve the process itself.
If there are redundant steps, unclear responsibilities, conflicting data sources, or late-stage validations, automation can amplify these issues at scale.
Before implementation, it is worth asking:
- Is this step still necessary?
- Why is the same information requested twice?
- Which system contains the correct data?
- Who resolves discrepancies?
- Which parts of the process add no value?
- Are we automating a temporary workaround that became permanent?
Sometimes, analysis leads to a simple but valuable conclusion: an agent is not needed. A conventional integration, business rule, or system improvement may solve the problem with less complexity.
What happens when the process is not ready.
Automating without analyzing exceptions and dependencies can transfer existing problems to the agent and scale them up. Even simple changes may impact billing systems, inventory, dashboards, or regulatory compliance.
It is also essential to understand what data truly represents. A zero value, for instance, may indicate an actual measurement—or that a sensor is under maintenance. If the agent does not know the difference, it may generate a plausible but incorrect recommendation.
“The most dangerous failure is the silent one: the agent produces something that seems correct but is not detected until much later.”, says Monzón, IA Lead at MindDen.
Additionally, if early versions generate false alarms or extra workload, teams may reject the system. That is why dependencies, exceptions, and validation mechanisms must be addressed before automation.
Before trusting it, it’s worth observing it.
Before allowing an agent to execute actions, a shadow phase can be established: the system analyzes real situations and generates recommendations, but does not yet intervene in the process.
Shadow phase: test before automation
During this phase, the following aspects are evaluated:
- The rate of false positives
- Missing information
- Uncovered exceptions
- Alignment with team decisions
- The usefulness and clarity of recommendations
- The potential impact of automated actions
The duration will depend on the volume and criticality of the process. A workflow with hundreds of daily operations can generate evidence quickly, while an industrial process with less frequent incidents will require a longer observation period.
Understand before automating.
The NIST AI Risk Management Framework structures this work around four functions: govern, map, measure, and manage. Although voluntary, it highlights a particularly relevant idea: risks and outcomes must be assessed throughout the entire lifecycle of the system and within its context of use.
An agent does not understand a process simply because it has access to large amounts of data. It requires a sufficiently accurate representation of its rules, exceptions, dependencies, and responsibilities.
In some cases, the analysis will confirm that the process is ready. In others, it will reveal the need to structure the data, clarify responsibilities, or redesign certain steps.
Both outcomes are valuable. The goal is not to introduce an agent into every process, but to identify where it can genuinely improve the way work is done.
“Without a baseline, any improvement is subjective”, our expert concludes.
In the next article, we will explore how to connect an agent with corporate applications while maintaining permissions, traceability, and human oversight.