Article
Why AI/LLM observability is becoming a critical tool for business
Author: Oleksandr Hohsadze, Enterprise Sales Manager, BAKOTECH
Content
● Is the traditional approach to monitoring no longer sufficient?● How can Dynatrace be useful? ● Conclusion
Today, we are witnessing a significant milestone: artificial intelligence and large language models (LLMs) are no longer merely topics of discussion or experimental playgrounds. Large businesses are actively integrating these technologies into customer services, internal process automation, and data analysis. However, moving into actual production brings new challenges that were not as critical previously.
In this article, I’ll explore why AI/LLM observability is becoming a key component of a successful AI strategy, the risks that arise without proper transparency and control, and how modern observability approaches help businesses scale innovation effectively.
Is the traditional approach to monitoring no longer sufficient?
Traditional IT systems behave predictably. If an error occurs, it can be reproduced, the root cause identified, and the issue resolved.
With artificial intelligence, things are different. Models can produce different results for the same queries, and their internal logic often remains a mystery even to their developers.
And this is precisely where the line is drawn between the successful AI implementation and unmanaged risks. Without a clear understanding of how the system works and makes decisions, it is difficult for businesses to guarantee consistent quality and security in their services.
It is also important to keep in mind that a modern AI application is not just a single model, but an entire stack of components: infrastructure, the model, data processing, the orchestration layer, and application logic. And a problem can arise at any of these levels.
How can Dynatrace be useful?
Working with the Dynatrace platform, we see that comprehensive AI/LLM observability is becoming the foundation for manageable innovation. In our experience, it is important to focus on three key aspects:
Business context and accuracy of results
For businesses, it’s not enough to simply know what the LLM told the user. It’s important to understand how relevant, accurate, and safe that response was.
Dynatrace provides end-to-end visibility into the operation of an AI application: from the user’s request through processing and the AI workflow to the final result. This enables the identification and analysis of incorrect or irrelevant responses (hallucinations) and, consequently, improves service quality.The economics of AI and ROI management
We often find that cost becomes a pressing concern after just the first few months of active LLM use.
Token consumption, infrastructure load, and query costs can rise rapidly and be difficult to predict. In such circumstances, it is important to have real-time cost transparency.
Dynatrace provides transparency: you can see the connection between costs and actual services—requests and business processes. This turns ROI management from theory into practice, allowing you to fine-tune your usage models.A new challenge: Agentic AI and autonomy
Today, we are entering an era of agent-based systems that do more than just generate text: they run processes, interact with other systems, and make decisions.
In this context, monitoring becomes a critical factor for stable operation and security. In addition to the results, it is important for businesses to understand exactly how the system performs.
Dynatrace’s data governance features and detailed audit trails enable you to track the actions the system performs and the contexts in which they occur. This is essential for security, transparency, and compliance.
Conclusion
The number of models deployed does not guarantee effectiveness. The success of AI transformation depends on the level of control over it. Only with complete visibility—from infrastructure to business outcomes—can a company confidently scale its innovations.
In our experience, questions about transparency, oversight, and how AI systems work are increasingly common in our conversations with clients.
If your company is already on the path to widespread AI adoption or if you’re facing challenges in monitoring your existing solutions, the experts at BAKOTECH are here to help.
I’d be happy to discuss your use cases and share my experience on how Dynatrace tools can help make AI transparent and effective. Feel free to reach out for a quick consultation—send me a private message or leave your questions in the comments.