As companies integrate artificial intelligence on a massive scale, the focus of IT leaders is shifting from simple automation to selecting the right tool for a specific process. This requires a strategic balance between deterministic orchestration (management based on clear rules) and probabilistic agents. According to the Cisco AI Readiness Index 2025, only 13% of organizations are classified as "Pacesetters"—leaders that consistently derive value from AI implementation due to a mature strategy.
The problem is that business leaders often choose inappropriate automation tools. For example, they may apply rigid algorithms to cognitive tasks or attempt to entrust critical compliance processes to AI agents. This leads to operational failures, the accumulation of technical debt, and serious security risks. The core error lies in the inability to distinguish between deterministic logic, where the result must be значна частина predictable, and the probabilistic nature of large language models (LLMs), where results vary.
Process mining as a first step: identifying the real process map
Designing automation based on subjective assumptions about how a business works is a common mistake. In practice, actual task execution differs significantly from documented regulations. To solve this, Process Mining technology is used. It reconstructs processes based on digital footprints in corporate systems and reveals how they are actually performed, highlighting bottlenecks and "shadow" routes that deviate from theoretical models.
Using Process Mining allows, for example, the identification of hidden bottlenecks in the supply chain before selecting an automation tool. This provides objective data for architectural decision-making, although it requires the expertise of business analysts to interpret the results correctly.
The trap of the universal tool: why all-in-one automation creates technical debt
The desire to find a single universal automation solution inevitably leads to problems. Applying a probabilistic tool (AI) to a deterministic process creates massive technical debt. Developers are forced to build complex code wrappers to strictly control every step of the agent. The result is an extremely fragile system. Conversely, attempting to automate cognitive activity using rigid rules in a traditional BPM leads to the creation of endless branches that break at the slightest change in input data.
Determinism versus probability: where BPMN ends and AI agents begin
To build an effective architecture, it is necessary to clearly distinguish between tools:
- Deterministic processes are governed by rigid rules. The standard for their orchestration is BPMN 2.0 (Business Process Model and Notation). BPMN 2.0 serves as an executable standard where a single model simultaneously documents and manages the process flow. To manage complex compliance rules that change independently of the main process, DMN (Decision Model and Notation) is used.
- Probabilistic processes are linked to cognitive activity. According to the Microsoft 2026 Work Trend Index, nearly 49% of interactions with AI assistants involve cognitive work: analysis, decision-making, and creative thinking. AI agents are capable of interpreting context and synthesizing unstructured data.
The correct approach involves delegating cognitive tasks to AI agents (such as summarizing documents) while maintaining deterministic control over routing and approvals within BPMN.
Security architecture: protecting hybrid workflows from prompt injection
The integration of AI agents creates new attack vectors. According to the OWASP Top 10 for GenAI applications (LLM01:2025), Prompt Injection—the insertion of malicious instructions via input data—is the top security risk. If an AI agent has direct access to critical systems, it can lead to unauthorized actions or data leaks.
To protect hybrid workflows, the principle of isolation must be followed: AI agents should operate as isolated services under the control of a deterministic filter. The technological foundation for such a hybrid approach can be the use of secure platforms. One example is the use of mechanisms within the UnityBase platform (a joint development by companies within the Intecracy Group, where InBase is a key developer). Through role-based access control (RBAC), row-level security (RLS), and detailed audit trails, the platform isolates data.
Specifically, the low-code platform Scriptum, developed on the basis of Camunda and utilizing UnityBase components, supports BPMN, CMMN, and DMN standards. This allows for a clear separation of deterministic logic while connecting AI agents and legacy robots (RPA) within a single secure architecture, minimizing the risks of Prompt Injection.
Selection matrix: a step-by-step algorithm for task distribution
To determine the optimal automation tool for a specific process, it is recommended to use the following matrix.
| Criterion | Low-code BPM (BPMN/DMN) | RPA (Robotic Process Automation) | AI agents (LLM/GenAI) |
|---|---|---|---|
| Process nature | Deterministic, strictly regulated, high compliance requirements | Routine, repetitive, working with interfaces without an API | Probabilistic, cognitive, analysis of unstructured data |
| Error risk level | Critical (significant tolerance for deviations) | Low (errors due to UI changes, requires monitoring) | Medium/High (requires human verification, risk of hallucinations) |
| Integration capabilities | Direct integration via API, system orchestration | Imitation of user actions in legacy systems | Intelligent context analysis, response generation |
| Application example | Credit limit approval, HR onboarding | Transferring data from Excel to an old ERP system | Initial complaint classification, document summarization |
By applying this approach, companies can optimize their automation efforts. RPA remains a temporary tool for legacy systems without APIs. AI agents take on the cognitive load, while low-code BPMN systems provide overall orchestration, security, and reliable execution of critical business rules.
FAQ
How do I know if a process requires an AI agent rather than a standard low-code scenario?
If a process requires cognitive work—analyzing unstructured data (text, complex documents) and making decisions based on broad context where rules cannot be formalized into a DMN table—an AI agent is needed. If the process relies on clear, predictable criteria, a deterministic low-code scenario is better.
Can RPA be completely replaced by AI agents when working with legacy systems?
No. AI agents work excellently with semantics but are not designed to reliably simulate clicks in legacy desktop interfaces without an API. RPA remains a necessary bridge for working with legacy systems, while AI can complement it, for example, by recognizing data before it is entered.
What security risks arise when integrating AI agents into corporate BPMN processes?
The main risk is Prompt Injection (LLM01:2025 according to the OWASP classification), where malicious instructions are passed to the system via input data. To protect the system, AI agents must be isolated from direct access to critical databases and transaction execution by using deterministic BPMN filters, access control (RBAC/RLS), and mandatory human-in-the-loop verification.