By 2027, first-line request processing in large organizations will inevitably transform from static scripts into dynamic systems based on AI agents. However, expectations for large language models (LLM) often clash with the harsh reality: granting agents full autonomy without clear systemic boundaries generates operational chaos. Businesses face a deep divide between ideal process models on paper and their chaotic implementation in practice, leading to errors, unpredictable behavior, and high security risks when AI agents are deployed without proper preparation.
Why AI agents will not replace BPM but become part of orchestration
At the beginning of the generative AI wave, there was an assumption that autonomous agents would completely replace traditional business process management (BPM) systems. Practice has shown otherwise: AI agents are effective at executing local steps, but they cannot independently maintain the overall transactional integrity of a process and its long-term state. They do not replace traditional BPM systems; instead, they become a new dynamic layer for task execution.
According to the Microsoft 2026 Work Trend Index Annual Report, 49% of conversations in Microsoft 365 Copilot supported cognitive work, including data analysis, decision-making, and creative thinking. This confirms that agents are becoming powerful assistants, but the success of their integration depends on strict orchestration. Rather than making final routing decisions independently, an AI agent should act as an intelligent operator at a specific step, while the overall logic of transitions is controlled by a classic process engine.
Analyzing reality before automation: the role of process mining and event logs
Before entrusting cognitive tasks to an AI agent, it is necessary to clearly understand the actual state of operations. Attempting to automate a process based on unrecorded shortcuts and workarounds (shadow processes) only scales errors.
Process mining becomes a mandatory step here. By analyzing digital traces from information systems (event logs), process mining, as noted by Celonis experts, allows for the identification of actual process flows, including bottlenecks and alternative routes. This is not a tool for automatic correction, but a means to create an evidence base. For example, comparing an "ideal" request processing scheme with actual logs often reveals how employees artificially close tasks to meet SLAs. Identifying such patterns allows for cleaning up the process before it is handed over to AI management.
Separating logic: how BPMN 2.0 and DMN contain the chaos of cognitive tasks
Standardization is essential for predictable interaction between BPM systems and AI agents. The BPMN 2.0 (Business Process Model and Notation) standard, according to Camunda, allows the same model to be used for both documentation and process execution management via a process engine, bridging the gap between description and technical implementation.
A key architectural step is separating decision-making logic (via DMN) from the direct execution flow (via BPMN). Instead of embedding routing rules into an AI agent's prompts, these rules are moved into DMN tables. When a company needs to change reimbursement limits or escalation rules, analysts simply update the DMN. The AI agent performs only the analysis of the request text and queries the DMN service for a verdict, which prevents unpredictable results.
First-line security: protection against prompt injection and data leaks per OWASP
Integrating LLMs into first-line support creates specific attack vectors because the system directly processes unstructured external requests. OWASP classifies Prompt Injection (LLM01:2025) as the primary and most critical risk for GenAI-based applications. An attacker can send an email with hidden instructions to force an agent to disclose sensitive information or change its operational logic.
An isolated architecture is required to mitigate this risk:
- Input request filtering: The AI agent must be placed behind a specialized API gateway that filters attempts at context manipulation before they reach the model.
- Data isolation: The agent should not have direct access to databases. All queries must pass through strict permission controls.
Organizational barrier: why culture and management matter more than technology
Even a flawless architecture can fail without the proper organizational foundation. According to Microsoft data, organizational factors such as culture and management support have twice the impact on AI implementation results than individual employee efforts. Success depends on leadership's willingness to revise KPIs and provide support to staff.
Practical implementation of such hybrid orchestration requires a reliable technological foundation. An example is the Scriptum platform (developed by InBase), which allows for the orchestration of processes according to BPMN 2.0 and DMN standards, integrating specialized AI agents from Softengi. The solution is built on the high-performance low-code platform UnityBase—a joint development by companies within the Intecracy Group technology alliance (an alliance of independent companies linked by partner agreements and share exchanges, where InBase acts as a key, but not sole, developer). Thanks to built-in UnityBase mechanisms, such as audit trails and flexible role-based access (RBAC, RLS), companies can integrate AI agents into a secure corporate perimeter, minimizing data leak risks.
Matrix of function distribution between the BPMN orchestrator and the AI agent
| Parameter | Role of the BPMN orchestrator (Scriptum) | Role of the AI agent (LLM/Cognitive) |
|---|---|---|
| Process state management | Full ownership of instance lifecycle and transitions | Local step execution within context |
| Decision-making | Rigid business rules based on DMN tables | Analysis of unstructured data and cognitive assessment (up to 49% of tasks) |
| Exception handling | Redirecting tasks to a human based on a fixed scenario | Attempting self-resolution within provided instructions |
| Security and access | Platform-level access control (UnityBase) | Filtering input requests to prevent prompt injection |
FAQ
How can an AI agent be integrated into an existing BPMN process without rewriting system code?
Integration is implemented by calling the AI agent's API services as a separate Service Task within the BPMN 2.0 diagram. The orchestrator passes only the necessary context to the agent, receives a structured response, and independently determines the subsequent route without losing control over the process state.
Which security risks according to the OWASP classification are critical when operating AI agents on the first line?
The most critical risk, according to the OWASP rating (LLM01:2025), is Prompt Injection—an attacker's attempt to inject malicious instructions via text queries. There are also risks of data leakage and unauthorized access, which require the implementation of request filtering and access control (e.g., RLS and RBAC).
Why is process mining a mandatory step before launching AI automation?
Process mining analyzes actual event logs and reveals the factual, rather than theoretical, state of processes. This allows for the elimination of "shadow" shortcuts and bottlenecks before the process is scaled and automated by artificial intelligence, reducing the likelihood of systemic errors.