The latest episode of Bitrock Tech Radio tackled a question that increasingly concerns teams with AI systems in production: how can we ensure these systems continue to work reliably over time? We discussed this with Michele Ridi, Chief Presale Officer of Fortitude Group and Bitrock.
Observability and Control Metrics
Imagine getting into a technologically advanced car that runs perfectly. Then you look at the dashboard and find it’s completely silent: no speedometer, no fuel gauge, no warning lights. You wouldn’t drive it on the highway, even if the mechanics were flawless. The risk would simply be too high.
That’s exactly what’s happening to many organizations that deployed AI systems without implementing AI Observability tools. The situation becomes even more critical when we talk about AI Agents — intelligent systems that don’t just answer questions but plan, execute actions, and make decisions autonomously.
Here’s the crucial point: this is no longer just a technical issue. With a simple chatbot, a hallucination produces a poor response that users see and can dismiss. With agentic AI, the picture changes entirely: an agent that fails doesn’t just write an incorrect sentence. It books a flight to the wrong destination, writes buggy code, moves data where it shouldn’t go, makes operational decisions based on flawed reasoning.
Control metrics and the ability to monitor what happens inside an AI system represent the control panel of this intelligent machine. Managing an agentic AI without observability means taking on a risk that’s simply unsustainable. Especially when you need to pass an audit, respond to a regulator, or answer to your board about what your AI system is actually doing.
What Model Degradation Means and Implies
Discussing model degradation brings up a concept that fundamentally changes how we think about AI compared to traditional software. With code we develop, we expect a certain stability over time: you write it, test it thoroughly, put it into production, and it continues doing what it’s supposed to do for years. That’s been our experience with software development so far.
AI doesn’t work that way.
A machine learning model or an LLM is trained on a specific scenario, with certain training data, in a particular market context, within a defined time window. The moment you put it in production, the external world keeps changing. Input data evolves, user behavior shifts, market conditions transform. But the model stays there, anchored to when it was created.
What happens is called drift — the phenomenon of derivation. There are fundamentally two forms of drift you need to understand:
- Data Drift: occurs when the data the model sees in production differs significantly from the data it was trained on.
- Concept Drift: an even more insidious phenomenon, because the relationship itself between variables changes over time. In reality the model needs to represent shifts, but the model remains static.
And that’s precisely where the real danger lies. The system keeps delivering answers with the same confidence as always, but the actual quality of those answers silently degrades. Nobody gets notified. Nobody notices until the degradation becomes severe enough to cause obvious errors. Or until it’s too late.
There’s also a second, equally silent risk: hidden biases that can emerge or amplify over time without anyone knowing. These biases weren’t there when the model was created, but they emerge as the model encounters new data and new scenarios. They can lead to incorrect, inequitable, or discriminatory decisions — not because anyone chose it, but simply as a consequence of the model’s drift over time.
Why Agentic AI Changes the Regulatory Game
With agentic AI, complexity increases significantly compared to traditional chatbots. These systems don’t simply process a question and provide an answer: they plan a response strategy, execute a sequence of steps autonomously, often dialogue with external tools, different systems, and even other models. A single request can trigger a complex chain of reasoning and actions.
When an agent operates this way, every error in a single step of the chain has the potential to propagate like a domino effect. A misinterpreted data point in one step, an ambiguous instruction passed to a tool, a tool called the wrong way — any of these can become a real-world action that’s hard to reverse. It’s not just a wrong sentence in a chat you can ignore.
This makes it essential to reconstruct what the agent thought, which steps it took, which tools it used, and most importantly, why. What experts call Agent Tracing — granular visibility into every reasoning and action of the agent. This isn’t simply a technical detail for specialists. It’s the difference between knowingly understanding why a decision was made and discovering the problem only when it’s obvious, or worse, during a compliance audit.
The Compliance Question
Another essential component comes into play here: the regulatory factor. The EU’s AI Act has completely transformed the landscape of AI regulation. The regulation classifies AI systems by risk level: there are prohibited systems, high-risk systems — those used in sensitive areas like employment or migration — that must meet very strict requirements, and then lower-risk or minimal-risk systems with less demanding obligations.
But here’s the critical point: it’s not enough to declare you’re compliant with the regulation — compliance must be demonstrable with continuous evidence. Not with a one-time audit, but with traceable proof you can show at any time. For high-risk systems, the AI Act explicitly requires risk assessment, accuracy, rigorous documentation, and data quality.
This means platforms managing AI systems must natively integrate observability, data integrity, and explainability capabilities. Not as optional features, not as add-ons, but as fundamental pillars of the system. These features serve teams not only to monitor and evaluate models and agents daily but to build the documentation and traceability that the AI Act literally demands. They’re the tools you use to demonstrate to regulators that your system works as designed and maintains quality over time.
A Verifiable Competitive Advantage
Taking AI observability seriously isn’t just a defensive measure against regulatory and reputational risks. For organizations that do it first, it becomes a concrete strategic advantage.
A company that can demonstrate its AI systems work exactly as designed, operate fairly, and maintain their performance over time transforms AI reliability into a verifiable difference. Something tangible that matters to the trust of clients, partners, and stakeholders — and it’s also the foundation for building strategic partnerships with organizations that demand guarantees about the quality of the AI systems they use.
The real challenge, though, comes after production launch. Putting AI into production is just one step of the journey. The real challenge is equipping yourself with the tools needed to govern it completely: from privacy protection to data access, from cost management to performance control, to regulatory compliance. Simply deploying a model isn’t enough: you need to govern it in every dimension.
In a context where agentic AI is becoming increasingly autonomous and sophisticated, and where the ability to demonstrate correct functioning becomes a regulatory requirement, ignoring observability isn’t just a technical risk. It’s a strategic risk that no modern organization can afford to take.
Bitrock, part of the Fortitude Group, helps enterprise organizations implement solid AI Observability strategies. With our proprietary Radicalbit platform and dedicated consulting, you can transform AI reliability into a verifiable advantage. Contact us for personalized guidance.