We’ve all been there, faced with the decision of how to automate a task. Should we rely on predictable business rules, or bring in the flexibility and power of an AI agent?
An AI agent refers to a system that leverages artificial intelligence, such as a large language model (LLM), to make decisions, interpret unstructured inputs, or perform reasoning tasks beyond traditional rule-based capabilities.
With Camunda 8.8, solving intelligent automation tasks is no longer limited to traditional rule engines. You can now choose between modeling deterministic logic using DMN decision tables, or invoking AI agents via connectors that integrate seamlessly with external LLM APIs. This native support for agent invocation through connectors makes it easy to embed language models into your BPMN workflows—allowing you to handle both structured decisions and complex, dynamic reasoning within the same orchestration platform.
As AI-powered orchestration becomes more common in enterprise automation, understanding when to use deterministic rules and when to invoke an AI agent—via Camunda—is critical for achieving the right balance of performance, governance, and adaptability.
In this post, I’ll walk you through how to decide whether a task is better suited for a DMN-based rules implementation or a dynamic, AI agent-driven approach—and how Camunda empowers you to design both.
Understanding the two abstractions
In the traditional BPM platform, rule-based tasks are typically modeled using DMN or Java delegates. They follow deterministic logic “if X, then Y.” They’re great when your process is stable, explainable, and highly regulated.
AI agents, on the other hand, can reason through uncertainty, summarize unstructured inputs, and make decisions without being hard-coded for every possible condition.
Each abstraction has strengths and trade-offs. Let’s dig in.
When to choose rule-based logic
Rule-based tasks (DMN-table) shine when:
- Decisions are deterministic (e.g., invoice under €500 → auto-approve).
- Auditing and compliance are key, since DMN models are traceable and explainable.
- Changes are frequent, but low-risk, and business users can modify rules via Modeler.
| Pros | Cons |
| Transparent and explainable | Cannot handle ambiguity or fuzzy inputs |
| Easy to test and maintain | Scalability suffers with complex logic trees |
| Lightweight execution | Hard to apply to natural language tasks or creative work |
Example of use case:
A company receives digital invoices via email or upload. The process should automatically decide whether an invoice can be approved without manual review. This decision depends on:
- The invoice amount
- Whether the vendor is trusted
- Whether a purchase order number is present
This logic is stable, well-documented, and governed by compliance policies. Therefore, we use only DMN to drive decisions.
Why DMN works here:
- The input is structured (invoice metadata)
- Decision logic is deterministic and explainable
- Easy to update with evolving policy changes
When to choose an AI agent
AI agents are better when:
- Tasks require natural language understanding (summarizing contracts, classifying emails).
- Inputs are messy or unstructured, documents, audio transcripts, free-form text.
- You need flexible, human-like decision-making, such as negotiation or prioritization.
| Pros | Cons |
| Adaptable and resilient in uncertain scenarios | Harder to govern and explain |
| Capable of zero-shot or few-shot learning | Requires testing with guardrails and feedback loops |
| Can generalize across use cases | Less predictable |
Example of use case:
The organization receives hundreds of emails daily from customers and partners. These emails vary in structure, language, tone, and intent, ranging from complaints, inquiries, and requests to spam. Predefined rules cannot capture all possible cases. The goal is to classify and route emails using an AI agent.
Why an AI agent works here
- Input is unstructured and semantically rich
- Rules would be brittle and hard to maintain
- AI agent adapts to new topics without re-modeling
Design pattern: mix and match
In practice, the most powerful automation comes from combining both approaches. You can model this hybrid approach in Camunda, a robust pattern that ensures explainability and flexibility.
Use rule-based logic to validate AI agent responses or trigger AI escalation paths.
Use AI agents to reduce the complexity of rule systems by pre-classifying or summarizing inputs before rules are applied.
Example of use case:
An insurance customer submits a claim after an incident ( car accident, stealing, or flooding). The claim includes:
- Structured data (policy type, amount, date etc.)
- Unstructured description of the incident.
The goal is to automate claim triage to decide whether to approve, reject, or escalate for manual review, with explainability and control.
Why combine?
- AI agent handles fuzzy tasks (classification, summarization)
- DMN ensures governance and traceability for business decisions
- Easier to audit downstream decisions, while keeping LLM as a preprocessing layer
This pattern strikes a balance between the flexibility of AI and the reliability of rules. With Operate and Optimize, you can not only view and analyze process execution, but also maintain full traceability—even when using AI agents. Inputs and outputs of LLM connectors are logged and visible, enabling you to audit decisions, monitor performance, and fine-tune AI agent behavior over time.
Conclusion
Choosing between rule-based tasks and AI agents isn’t a binary decision, it’s a strategic design choice. The best automations often start with rules and grow into agentic processes as complexity increases. As this becomes necessary, agentic orchestration enables agents to be added, monitored and governed easily within any process, ensuring flexibility without losing control.
Understanding the abstraction trade-offs helps you design smarter, safer, and more scalable AI-powered processes with Camunda.
Key takeaways
- Use rule-based logic for deterministic, auditable decisions.
- Use AI agents when dealing with unstructured data or complex reasoning tasks.
- Combine both approaches to get the best of both worlds: explainability + adaptability.
- Test and monitor AI agent behavior with human-in-the-loop and feedback loops.
Ready to see hybrid automation in action?
Try modeling your first AI-powered process in Camunda using this AI Email Support Agent template from the Camunda Marketplace.
Already experimenting?
Share your experiences and learn from others in the Camunda Community Forum.
Start the discussion at forum.camunda.io