What is LLM Observability? Discussing AI Observability, Agent Observability, and the Journey to Reliable AI 

Innovation may drive efficiency, but that doesn’t mean it drives clarity. One of the hallmarks of an emergent technology is the escalation of terms. And what’s sometimes called “LLM observability” is no exception. 

When any category evolves as rapidly as observability for AI systems, it can be difficult to unite around the language of the thing. New products are being released by the minute. What once defined the problem is realized not to be the problem at all. It’s natural for the terms to get a little…inconsistent.

But what we call a solution says a lot about how we think about solving the problem; aim too narrow and you might miss key functionality; too wide and you risk not solving anything at all.

LLM observability may be one way to talk about the solution for AI trust, but is it the right way? In this article, I want to define the term LLM observability in its context, how it relates to the broader category of AI observability tools, and ultimately why it might not be the best way to classify a solution for reliable production AI.

Sounds interesting enough? Great! Let’s get started. 

What is LLM Observability?

LLM Observability is the practice of validating AI systems in production by monitoring for issues like slow performance and prompt accuracy and tracing the origin of those outputs from source to response.

Sounds fair enough. So, what’s the problem? While LLM observability might be directionally correct in its emphasis, it’s technically misleading in its ultimate implications. Let’s go deeper

A critique of the term

As you might have guessed from my obviously leading question, I think LLM observability is a bit of a misnomer. An unfortunate affectation of product teams smashing buzzy terms together to cash in on some quick SEO juice.

Let’s just consider the term “observability” for a second. If we review other uses of the term, we see a clear emphasis on end-to-end visibility. Data observability provides coverage for your entire data platform. Software observability provides coverage for your entire software ecosystem. In each of these examples, observability implies an ability to observe the entire system of a given product—the end-product that’s delivering the value. 

But the LLM isn’t your product. (At least I hope it isn’t.) The LLM isn’t the product to be observed—it’s a single component in a complex network of components that supports that product. 

It’s no more significant to the system than the orchestration or ingestion tooling upstream. If all you have is an API call to Gemini, you don’t have enterprise AI—you have a reskinned Gemini.

But the biggest problem of all? And this one’s a real pickle—the LLM can’t actually be observed by your tooling.

Let’s look at a couple of components of LLM observability to get a little more specific. 

Components of LLM Observability—and what they really tell us

When teams talk about observing an agent or model, they’re generally talking about 2 primary things—evaluations and tracing. 

Evaluations

Let’s ask the obvious question first—what are evaluations actually evaluating? No big surprise here—it’s not the LLM. It’s the output.

In basic terms, evaluations are monitors that use AI to evaluate AI (LLM-as-judge), and they’re generally used to analyze qualities of an output that can’t be quantified using basic code: things like sentiment analysis, tone, prompt alignment, etc. You’re not evaluating the foundational model at large—you’re evaluating your own agent’s outputs. 

Tracing

Similar to the lineage that explains a data product, tracing is intended to demonstrate the lineage of specific output through your pipelines. But if we’re talking about enterprise AI (agents, CS chatbots, whatever), the answer isn’t coming from the general corpus of information available in the foundational model, it’s coming from your first-party data.

The simple fact that you have a trace demonstrates that the output came from your own data. If a trace isn’t present, it demonstrates that the output came from the LLM, not your data—which would make that output wrong, not right. The only way you can “trace” an output from a foundational model is by acknowledging that no trace is present. 

That’s not tracing, that’s anti-tracing.

So, what is the best tool for LLM Observability?

The best tool for LLM observability isn’t LLM observability at all. It’s an end-to-end observability solution that unites the entire AI system from source to output. 

LLMs aren’t like any software you’ve monitored before. They’re a magicians hat. You can put your hand in, but you can’t decide what kind of rabbit comes out. The creators aren’t even sure how these technologies work. 

And because that AI is just a black box of probabilities, what goes into the box is just as important as what comes out of it. Data and models are two halves of the same system—and you’re either validating both…or you aren’t validating anything at all.

SO, if we want to make AI reliable, we need to go beyond what “LLM observability” implies. 

Enterprise data and AI teams need a single observability platform that can monitor the data that feeds their agents (not the LLM) and the outputs those systems collectively generate.

Anything less and you don’t have observability. 

3 LLM observability tools

Here are a few observability tools that could be considered “LLM observability tools”:

Arize AI

Arize AI provides real-time performance monitoring and drift detection for machine learning models in production. Its AI observability tools leverage open standards and include specialized support for large language models (LLMs). However Arize lacks the end-to-end cover at the source to be considered observability. 

New Relic

New Relic extends its leading observability cloud to support comprehensive monitoring of AI pipelines, capturing detailed metrics such as latency, throughput, and cost per model call. The solution emphasizes proactive performance management, helping teams optimize AI efficiency and reliability. But while it incorporates telemetry from software applications, it still falls short at the most essential data levels. 

Monte Carlo

Monte Carlo is the only “LLM observability” solution that aims to effectively combine these two systems together in a single platform. AI-powered anomaly detection, automated root-cause analysis, end-to-end lineage tracking, and extensive integrations with modern data + AI stacks give teams visibility from source to agent—providing essential functionality for AI-ready data and production-ready agents. 

LLM Observability vs Agent Observability

AI agent observability is an observability use case that provides visibility and management tooling for both the inputs and outputs of a given AI agent as well as the performance of its component parts. 

While AI observability is a broader category of observability for AI applications, agent observability’s primary goal is to make AI agents production-ready. Tools like Monte Carlo provide agent observability that delivers coverage from source to agent, including the data that trains and provides the embeddings and the model that activates that data to generate a response.

LLM Observability vs AI Observability

AI observability and LLM observability are often used interchangeably to refer to AIOps platforms that provide visibility, management, and operational tooling for AI applications. However, because “observability” indicates coverage for an entire ecosystem, AI observability broadly or agent observability specifically are the better solutions here. 

RAG (retrieval augmented generation) observability also refers to a similar but slightly less narrow pattern that includes an AI or agent retrieving context for an embedding. Other terms could include LLMops, AgentOps, or evaluation platforms.

Check out Gartner’s “Innovation Insight: LLM Observability” for a similar definition of terms. 

Conclusion

Much like the AI industry at large, the lexicon for AI reliability tooling has evolved rapidly since 2023. Platform providers make their living by obfuscating language—couching their product in opaque or nonspecific terms that will make their solution seem more complex or mystical than it really is. 

As of this moment no platform provider is selling true LLM observability. And I’m going to make a bet right now that they never will. 

You need a solution that goes beyond the language on your big tech bingo card—that communicates a clear vision for the problem, a practical customer-first product that solves it, and the receipts to back it all up. 

The proof is in the pudding. And that pudding is all over analyst reports, customers stories, and review sites like G2… if you’re willing to look. 

Don’t settle for talk. Find out how Monte Carlo’s unified data observability and agent observability platforms can help you deliver AI-ready data and production-ready AI in one fell swoop.

Our promise: we will show you the product.