The Future of AI Monitoring: How to Address a Non-Negotiable, Yet Still Developing, Requirement 

Generative AI models are not just tools for producing text, audio or video—they're systems that learn patterns, improvise, and generate unexpected outcomes. When we look at LLMs, we're struck by their capacity to generate surprisingly creative and context-aware results. They can weave coherent narratives, propose novel solutions, mimic human conversation, and even offer nuanced insights across a wide range of topics. While this creativity is their strength, it also introduces variability and risk. The challenge for enterprises isn't just harnessing the power of generative AI—it's making sure that power is predictable, reliable, and safe at scale.

Creative Geniuses Genius Statisticians 

But unlike human minds, LLM outputs are the results of advanced mathematical calculations across billions of parameters interacting in complex, non-linear ways to predict the most probable next word. Their "creativity" is statistical inference across massive datasets.

Unlike traditional rule-based systems, LLMs perform statistical inference without following predefined logical rules like tra enable decision trees. This allows them to generate outputs based purely on patterns learned from data rather than strict instructions.

This means LLMs succeed in generating such creative outputs because they are intentionally designed to operate without rigid, hard-coded logic or constant supervision. It is their unmonitored and open-ended nature that stimulates and powers their performance. 

The Drawbacks of LLM Creativity

This statistical freedom is precisely what gives AI systems their power, but also what makes them so challenging to govern. LLMs operate as black boxes, making it extremely hard to trace back why a certain output occurred.

This "black box" quality results in creative results, but it means LLMs can hallucinate, exhibit bias, or even leak sensitive patterns from training data. They can generate responses that are factually incorrect, biased, or even non-compliant with regulatory frameworks like GDPR, HIPAA, or industry-specific standards.

As a result, businesses risk reputational damage, legal liabilities, and flawed decision-making based on inaccurate outputs. This is especially important in high-stakes industries like finance, healthcare, e-commerce and legal.

An Evolving Landscape for AI Monitoring

To account for the risks inherent in these advanced AI systems, enterprises must implement a system that governs outputs. This is monitoring.

Monitoring is the continuous observation of the outputs and behaviors of AI systems to ensure they align with ethical standards, business objectives and regulatory requirements. For the enterprise AI world, monitoring on deterministic AI systems is fairly straightforward, as outputs can be measured against ground truth. Monitoring for gen AI systems is new territory, including checking for hallucinations, bias, misuse, compliance violations, security risks and even performance degradation over time.

Organizations must actively monitor LLMs to ensure trust accuracy, and compliance, especially when deploying them in business-critical environments. Monitoring provides assurance that AI-driven interactions are responsible, ethical and high quality, turning generative AI applications from unpredictable black boxes into controlled, auditable, and trustworthy tools for real-world use.

Why is Monitoring Often Forgotten?

Organizations often overlook monitoring for LLMs until the eleventh hour because of the initial excitement around capability over control. When teams first adopt generative AI, the focus is usually on speed, innovation and showcasing what the model can do, like automating workflows or powering chatbots. Governance, monitoring, and safety measures are seen as blockers to progress rather than enablers of sustainable use.

There’s also a false sense of security that comes from the slickness of the output. LLMs can sound incredibly confident, even when they’re wrong. Many teams don’t realize the risk until something breaks: a hallucinated fact in a report, a customer-facing bot giving bad advice, or an AI-generated document violating privacy or compliance norms.

Another reason is that monitoring LLMs is very difficult. It’s not as simple as setting up alerts or dashboards. As discussed, these models don’t explain their reasoning, and their outputs can’t always be evaluated with traditional QA or rules-based logic.  LLMs operate by predicting the next word based on statistical patterns learned from massive datasets. Even the developers can’t fully explain the model’s reasoning.

AI Monitoring Ground Rules

There is no one solution for LLM monitoring in general, but there are some basic principles of designing such a system. Most use cases will require multiple monitoring methods in order to understand where problems exist. Therefore, the solution architecture must be capable of applying multiple guardrails and changing them over time without changing the rest of the pipeline. Creating a guardrail library that can be shared across pipelines will give your team infinite flexibility to add guardrails as needed. The ideal approach should be to add guardrails as easily as building that nice demo.

Based on my experience working with hundreds of organizations on monitoring their LLMs, there are three non-negotiable prerequisites for any monitoring solution:

• Implementing guardrails at both the input and output stages - Input guardrails help ensure that prompts don’t introduce unsafe, biased, or unintended scenarios that could nudge the model into problematic responses. Output guardrails act as a final checkpoint before the model’s response reaches the user, filtering out harmful, off-topic, or non-compliant content.
• Ensuring that performance remains within acceptable boundaries - Start by  defining what "acceptable performance" looks like across key metrics: relevance, coherence, factuality, latency, etc. If crossed, trigger alerts, interventions, or automated responses. especially in production environments.
• Verifying that every output includes the relevant guardrails - This means having mechanisms in place, like  automated tests, post-processing checks, Human-in-the-Loop to validate that every single response aligns with safety, compliance, ethical and performance standards.

Where Do We Go From Here?

Many new players in this space offer everything from real-time output logging, prompt tracing and evaluation dashboards to feedback collection and performance scoring. Some plug directly into prompt pipelines, others offer tools for evaluating safety, factuality, or bias post-hoc.

We built MLRun, an open source AI orchestration tool, to integrate with multiple monitoring tools because we believe that when it comes to LLMs and gen AI, monitoring isn’t optional. But instead of keeping users in a rigid, one-size-fits-all system, MLRun gives you the flexibility to monitor your models your way: use multiple solutions, easily swap them out, and avoid lock-in.

MLRun allows you to plug in your favorite external monitoring tools, like logging platforms, alerting systems, or metric trackers, through APIs and simple integration points. This is especially important when you're tracking custom KPIs or operational signals that might go beyond standard built-in dashboards.

Currently, there is no one-size-fits-all solution. But the LLM ecosystem is evolving rapidly, and model monitoring is emerging as one of its most critical layers.