When people first encounter AI agents in enterprise platforms, the most common model is conversational. A user asks something, the agent interprets the intent, and then invokes a workflow or action. This makes sense, because the interaction starts with language, and the agent becomes the entry point into the system.
But the opposite pattern is important too. Workflows can invoke AI agents.
In this case, the workflow is still the starting point. A record is created, a form is submitted, or an integration brings in data. The workflow begins execution, following its predefined logic. But at some point in that process, the workflow may need interpretation. It may need to summarize content, classify a request, or generate a response. This is where the workflow calls the agent.
The agent, in this scenario, is not orchestrating the process. It becomes a capability used by the workflow. The workflow provides the data, the agent interprets or transforms it, and then the workflow continues execution based on the result.
This reinforces an important architectural distinction. The workflow remains the execution engine. The agent becomes an interpretation layer that can be invoked when needed.
This pattern is useful because not every process begins with a conversation. Many processes begin with events: a ticket created, an email received, a record updated. These events still benefit from interpretation, but the overall control flow remains deterministic.
Understanding both directions clarifies the role of agents in enterprise systems. Agents can invoke workflows when interaction starts with intent, and workflows can invoke agents when execution requires interpretation. In both cases, the workflow remains the structure that ensures reliable and controlled execution, while the agent provides the flexibility to understand and work with unstructured data.
Agents do not replace workflows. They extend what workflows can understand.
Jesús Melendez 