AI workflow automation: Scalable use of AI agents

What are agentic workflows and what is AI workflow automation?

AI workflows refer to automated process chains in which artificial intelligence independently makes decisions or performs tasks. The aim is to speed up processes, analyse data and reduce manual work.

Agentic workflow automation goes one step further. It combines classic process automation with generative AI. An AI agent acts similarly to a digital employee who interprets information, draws conclusions and initiates actions independently. This results in dynamic processes that can be flexibly adapted to different contexts.

In this context, the term orchestration plays a crucial role. In traditional workflow systems, the orchestrator is a clearly defined mechanism that coordinates each step according to transparent rules. Every action is traceable and auditable, which means that the entire process can be made visible to users or administrators if desired. This transparency provides a high level of reliability and reproducibility.

In contrast, the orchestrator within an agentic workflow is itself an AI agent. While this makes it far more flexible and adaptive, it also introduces a degree of unpredictability and opacity. The state and decision-making process of such an orchestrator are not fully traceable, nor can its reasoning always be reproduced. This creates a trade-off between flexibility and control—a balance that current AI workflows often struggle to maintain.

In theory, this is considered a step forward in AI-supported automation. In practice, however, AI workflows in their current form are not always efficient, reliable or cost-effective.

Limitations of Agentic Workflow Automation

The current hype surrounding AI workflows has spawned numerous tools that advertise fully automated, AI-based processes. But on closer inspection, crucial weaknesses become apparent.

The biggest problem is cost-effectiveness. Most solutions rely on AI at every step and charge for tokens, credits or API calls. This means that even simple tasks such as forwarding data or executing logical decisions are calculated using AI. As soon as companies try to scale up these processes, the initially attractive licence packages are no longer sufficient and high costs await.

Added to this is the risk of unpredictable results. When every step of the process is interpreted by AI, so-called hallucinations can occur. This refers to invented content or incorrect connections that the AI generates in order to provide a satisfactory answer. This jeopardises the reliability and traceability of workflows and also means that tokens are used even for simple logical queries.

Another weakness is that many agentic workflows are entirely AI-based. There is no clear boundary between deterministic logic and creative thinking. However, companies need structured processes in which critical decisions remain reproducible and defined procedures are adhered to without creative input.

The future of AI workflow automation therefore lies not in complete dependence on AI, but in combining clear process logic with targeted, intelligent use of AI.

FlowDrop as a response to the weaknesses of traditional AI workflows

AI workflows are a central component of modern process automation, but their current form has limitations. That is why we have developed our own solution for complex AI workflows using agents: FlowDrop.

FlowDrop is an open-source tool that enables complex workflows to be set up by combining clear process logic with targeted AI use. The result is an intelligent, cost-efficient and controllable solution for companies that want to future-proof their digital processes.

What makes it special is the targeted separation of automation and artificial intelligence. FlowDrop only uses AI when interpretation, analysis, research, generation or contextual understanding are required. In all other cases, it relies on stable, rule-based process logic.

This combination reduces costs, minimises the error rate caused by excessive AI use and creates traceable workflows that remain scalable and efficient. The result is a new model of intelligent process automation that is both creative and controlled.

The current version of FlowDrop can be integrated directly into the Drupal content management system (CMS) and connects AI agents with a clearly defined automation model. Integrations into other software systems are already on the roadmap. Our solution approach with FlowDrop shows that AI workflows do not have to be based exclusively on artificial intelligence to enable scalable process automation.

The true strength of AI workflow automation lies in the interplay of generative intelligence and logical precision.