The Guide to AI Observability: Best Practices, Challenges, Tips, and More

If there’s one thing that’s true about AI, it’s this: what performs perfectly in testing will never perform perfectly in production.

Credit approvals, customer recommendations, operational forecasts. AI and agents workflows can be used to supercharge all kinds of tedious and repetitive workflows. You probably know they’re running. You might even track their response times. But how do you know they’re making good decisions? Who’s sounding the alarm when the accuracy starts to slip?

The systems may be sophisticated but the native tooling teams are leveraging to monitor them is not. Performance metrics show green lights while the actual decision quality deteriorates. Data drift goes unnoticed for months. Bias creeps in without anyone realizing. What’s worse, most of these AI systems still operate as a black box. While you might be able to identify a bad output (with the right human in the loop), you won’t be any closer to understanding why it happened or how to resolve it.

This delta between what we think our AI is doing and what it’s actually doing is a zero-day risk for data and AI teams. Financial services have lost millions on undetected model degradation. Insurance companies have made coverage decision with outputs they couldn’t verify. The list goes on. Unreliable AI is coming for every industry. And like the data industry before it, we need a scalable observability solution to detect, manage, and resolve it at scale.

AI observability provides complete visibility into your AI pipeline, from data quality to model outputs. In this article, we’ll discuss what AI observability is, how to select the right solution, and what it looks to operationalize AI observability at scale. We’ll also cover some of the practical challenges organizations are facing, from balancing transparency with privacy to making monitoring accessible for non-technical teams, and proven strategies to resolve them across enterprise teams.

What is AI observability?

AI observability is the practice of monitoring artificial intelligence applications from source to embedding. When used correctly, it provides complete end-to-end visibility into both the health and performance of AI in production, so that teams understand what went wrong, why, and an how to fix it.

So, what is AI observability monitoring specifically? Let’s start by considering traditional software observability.

Traditional software observability asks three main questions.

• Is the application running?
• How fast is it performing?
• Are there any errors?

However, unlike traditional software applications, AI applications and agents are neither static nor deterministic.

Structured and unstructured data model inputs change constantly. Outputs are probabilistic by nature. Pipelines can traverse a multitude of systems and teams with limited oversight. And even the smallest issues in data, embeddings, prompts, or models can lead to dramatic shifts in a system’s behavior. What’s more, these systems still operate largely as a black box. You might be able to identify when an output goes bad if you the right monitoring in place, but you’re unlikely to know why or how.

That means that defining performance and reliability isn’t as simple as validating the inputs and outputs. In order to validate the reliability of AI, we need to be able to determine not just the difference between right and wrong, but the difference between right and mostly right—and which of the dynamic systems and embeddings caused it. Some questions AI observability might aim to answer:

• Is the AI making good decisions?
• Is it treating different groups of people fairly?
• Are predictions getting less accurate over time?

Everything about traditional software engineering is determined by decision tree. If the system slows or fails, a simple yes/no testing framework is all that’s required to sound the alarm. When it comes to AI, the only thing that’s truly deterministic is whether or not it delivers a response; not the usefulness of the response it delivers. Once prompted, the AI itself is free to decide the what, why, and how of its response. And when it comes to agents in production, that can all happen autonomously without any oversight at all. And as long as that agent continues consuming credits and delivering outputs, there’s often no accounting for whether that agent is silently making terrible decisions in the process.

A hiring agent might process thousands of resumes without any technical errors, but if it favors one demographic group over another, it’s still failing in a big way. The only difference is that you wouldn’t know about it. And that’s where AI observability steps in. Standard monitoring tools would still show green lights across the board. AI observability would catch the bias red-handed.

Why AI observability is important

The rapid adoption of AI has created tremendous opportunity for the enterprise teams that can wield it, but it’s also created tremendous risk.

Seemingly overnight, AI applications have been deployed across every sphere of industry, from customer service chatbots to live-saving medical devices, often without the tooling or process to understand how they’re performing at production scale. And that would be bad enough on its own. But the problem isn’t simply that most teams don’t have the right safeguards in place; what’s worse is that most teams aren’t even sure what safeguards should be in the first place.

While no technology will ever perform perfectly 100% of the time, it’s the uniquely opaque ways in which an AI can fail that make it particularly challenging to maintain without an AI observability solution that automates the reliability workflows required to deliver AI to production.

Poor data quality alone costs organizations an average of $12.9 million per year, according to Gartner. And a recent Forrester Total Economic Impact study found that organizations without proper AI observability face an additional $1.5 million in lost revenue due to data downtime annually. Add in the cost of biased AI decisioning, regulatory penalties, and lost trust, and the true cost of invisible AI failures is exponential.

And regulatory pressure is intensifying this urgency. The European Union’s AI Act, which began taking effect in 2024, mandates continuous monitoring of high-risk AI applications. Similar laws are developing across the United States, Canada, and other nations. If you were thinking AI reliability was optional (it wasn’t), governments are making it non-negotiable.

However, the most important reason why you need AI observability is because Ai observability allows organizations to deploy artificial intelligence responsibly.

As AI applications tackle sensitive choices about hiring, lending, healthcare, beyond, visibility becomes your first and only line of defense. When you can see how your AI makes decisions, you’ll be better equipped to improve those decisions over time; from eliminating bias to elevating cost performance.

Key components of AI observability

AI can go bad in a lot of ways, and they aren’t always in the model itself. In fact, much of what portends to be an AI failure is often a data issue in disguise. Some definitions of AI observability will limit visibility to the model itself, this last-mile mentality is ultimately incapable of providing sufficient coverage for all the ways AI can break—or resources to resolve it when it does. While the AI output might be the final product, it’s everything that makes up that output that defines its reliability and its fitness for a given use case.

In the same way you can’t make soup with just a bowl water, you can’t make AI observability by only monitoring the output. Monitoring the output is certainly one critical ingredient of the observability recipe—but it’s not the only one. Creating reliable AI applications requires monitoring each of the four interdependent components that comprise their pipelines: data, system, code, and model response.

These four components work together to provide complete visibility into the health and performance of production AI applications. And when any one of these components is overlooked it can cascade into all kinds of silent failures that will be much more difficult to detect, and even more challenging to resolve at scale.

The good news is that much of what has defined traditional data observability forms the foundation of AI observability. The unification of the data and AI systems into a single pane of glass create a unified framework that organizations will need to master in order to scale their AI and agents into production.

Observing data

AI is fundamentally a data product. Both foundation models and enterprise AI applications depend on vast collections of structured and unstructured data to create useful outputs. From initial model training to the retrieval processes that feed current information to AI applications, data quality determines everything that follows.

Data observability has already proven itself as the primary solution for maintaining data health at enterprise scale. According to Gartner’s AI-readiness research, modern data quality tools have become the foundation for production-ready AI applications. This makes sense because any problems in your data will directly impact your AI’s performance, often in subtle ways that are hard to detect.

Monitoring data pipelines means watching for anomalies in data volume, detecting when data sources change format unexpectedly, and identifying when information becomes stale or corrupted. For AI applications, this also includes monitoring the specific data feeds that power retrieval processes and ensuring that training data remains representative of current conditions.

Observing infrastructure

AI applications rely on a complex network of interconnected tools and platforms to function properly. Your typical AI stack might include traditional enterprise data platform layers, (data warehouses, transformation tools like dbt, observability platforms like Monte Carlo, etc), alongside vector databases (where embeddings and high-dimensional data live alongside traditional structured data,) and context databases that store institutional knowledge that informs AI decisions. AI systems will consume all this rich data, then enter experimentation loops, with reinforcement learning training agents to navigate complex enterprise environments.

This goes deeper than traditional application monitoring or even data observability. GPU utilization patterns, memory consumption for large models, and the performance of vector databases all require specialized attention. A slowdown in any one component can cascade through the entire AI application.

And the interconnected nature of AI infrastructure means that problems often originate in unexpected places. A minor configuration change in your data transformation tool might not break anything immediately but could gradually degrade your AI’s performance over weeks as the quality of processed data slowly declines.

Organizations that implement infrastructure monitoring across their AI stack see dramatic efficiency gains. Monte Carlo’s Monitoring Agent, for instance, increases monitoring deployment efficiency by 30 percent or more, while automated anomaly detection reduces the time teams spend on manual configuration. Nasdaq achieved a 90% reduction in time spent on data quality issues, translating to $2.7M in savings through improved operational efficiency.

Observing code

Code problems in AI applications extend far past traditional software bugs. While bad deployments and schema changes can still wreak havoc on AI pipelines, AI introduces entirely new categories of code that need monitoring. This includes the SQL queries that move and transform data, the application code that controls AI agents, and the natural language prompts that trigger model responses.

Prompt engineering has become a form of programming, and like any code, prompts can break in subtle ways. A small change in how you phrase a request to an AI model can dramatically alter the quality and consistency of responses. Traditional code monitoring tools aren’t designed to catch these kinds of failures.

Version control and testing become more complex when your “code” includes natural language instructions. Organizations need to track changes to prompts, test them in a structured way, and monitor their performance in production just like any other critical code component.

Observing model outputs

Model responses represent the customer-facing product of your AI application, but monitoring them requires entirely new approaches. Unlike traditional software outputs that either work or fail clearly, AI responses exist on a spectrum of quality that can be difficult to measure automatically.

Monitoring model performance means tracking metrics like response relevance, accuracy, and consistency over time. This includes watching for model drift, where performance gradually degrades as real-world conditions change from what the model learned during training. It also means monitoring for bias, ensuring that the AI treats different groups of users fairly.

The challenge is that many of these quality measures require human judgment or sophisticated evaluation frameworks. Organizations need to develop ways to sample and evaluate AI responses regularly, create feedback loops that capture when the AI makes mistakes, and build automated processes that can detect when response quality starts declining.

The business impact of model output monitoring is substantial. According to Forrester’s Total Economic Impact study, organizations that implement AI observability achieve an 80% reduction in data and AI downtime, with a 90% improvement in data quality issue resolution.

Critical AI observability features

AI observability can be built internally by engineering teams or purchased from a third party. Whether you choose to build or buy AI observability will depend on the capabilities of your team, the scale of your use case, and where in the deployment funnel your particular pilot might be. Similar to data testing, some smaller teams may choose to start with an internal build until they reach a scale where a more systemic approach is required. While home-built Ai observability solutions are often fine in testing, these solutions may create silos that limit visibility and impede reliability in production, so keep that in mind as you consider your own build vs buy scenario.

But whether you choose to build internally or engage a platform provider, there are basically two core components above and beyond traditional data observability that address the last mile of AI observability specifically: trace visualization, evaluation monitors, and context engineering.

AI tracing

Similar to lineage for data pipelines, traces (or the telemetry data that describes each step taken by an agent) can be captured using an open source SDK that leverages the OpenTelemetry (Otel) framework. Monte Carlo offers one such SDK that can be freely leveraged, with no vendor lock-in. Here’s how it works:

• Step 1. Teams label key steps, like skills, workflows, or tool calls as spans.
• Step 2. When a session starts, the agent calls the SDK which captures all the associated telemetry for each span such as model version, duration, tokens, etc. 
• Step 3. A collector sends the data to the intended destination (generally a warehouse or bakehouse), where an application can help visualize the information for exploration and discovery.

One benefit to observing agent architectures is that this telemetry is relatively consolidated and easy to access via LLM orchestration frameworks as compared to observing data architectures where critical metadata may be spread across a half dozen systems.

AI evaluation monitors

Once you have all your agent telemetry in place, you can monitor or evaluate it using another AI. Sometimes this can be done using native capabilities within data + AI platforms, but a siloed evaluation is not recommended for production use cases since it can’t be tied to the holistic performance of the agent at scale or used to root-cause and resolve issues in a scaled context.

Teams will typically refer to this process of using AI to monitor AI as an evaluation. This tactic is excellent for monitoring sentiment in generative responses. Some dimensions you might choose to monitor are:

• helpfulness
• validity
• accuracy
• relevance
• etc

This is because the outputs are typically larger text fields and non-deterministic, making traditional SQL based monitors less effective across these dimensions.

Of course, evaluating the output is only a fraction of the problem. Again, the output is only the last mile of the AI journey. SQL monitors are critical for detecting issues across operational metrics like system failures and cost, as well as situations in which the agent’s output must conform to a very specific format or rule (like US postal codes). And in cases where either tactic would be performant, opt for deterministic code-based monitors. A good rule of thumb: if you can do it with code, use code. Not only will you be able to understand the if/then nature of the response, but you’ll enjoy the added benefit of reducing the cost to monitor for a given dimension.

Context engineering and reference data

Again, the model is the end of the journey, but it’s not the journey itself. Drawing a firm line between where data observability end and AI observability begins is almost impossible. These two twin systems are more like a Venn diagram than separate tools. They need to be unified and managed together in order to be effective—and context engineering is the linchpin.

At its core, AI is a data product. Outputs are determined (albeit probabilistically) by the data it retrieves, summarizes, or reasons over. In many cases, the “inputs” that shape an agent’s responses — things like vector embeddings, retrieval pipelines, and structured lookup tables — are part of both data and AI resources at once.

This idea is based articulated in one ubiquitous phrase: garbage in, garbage out. An agent can’t get the right answer if it’s fed wrong or incomplete context; something LLM-as-judge evaluations are woefully inadequate to detect.

To learn more about observing data and AI together, check out our O’Reilly report “Ensuring Data + AI Reliability Through Observability”.

How to implement AI observability in your organization

Implementing data plus AI observability isn’t something you can do overnight, but it doesn’t have to be overwhelming either. Most organizations already have some monitoring in place for their data pipelines and applications. The key is extending that foundation to cover the unique challenges that AI applications present.

The biggest mistake organizations make is trying to monitor everything at once. Instead, start with your most critical AI applications and build outward. Focus on the AI tools that directly impact customers or business operations, then expand your monitoring as you learn what works best for your specific environment.

Success comes from taking a methodical approach that builds on what you already have while adding the AI-specific monitoring capabilities you need. Here’s how to get started without disrupting your current operations.

Organizations that follow this methodical approach see rapid returns on their investment. Forrester’s analysis shows a 357% ROI over three years with a payback period of less than six months. JetBlue, for example, achieved a 16-point NPS increase in under one year by implementing data plus AI observability practices.

Assess your current data and AI setup

Before you can monitor your AI applications successfully, you need to understand what you’re actually running. Many organizations discover they have more AI components than they realized once they start mapping their technology stack. This assessment phase is about creating a complete picture of your current operations and identifying where the biggest risks lie.

Start by cataloging all your AI applications, from customer-facing chatbots to internal analytics tools. Document how data flows through each application, which data platforms they connect to, what external services they depend on, and which teams are responsible for maintaining them. This inventory often reveals surprising connections between different applications that share data sources or infrastructure components.

Select the right data monitoring tools

The monitoring tools that worked for traditional applications won’t be sufficient for AI applications. You need platforms that can handle the unique requirements of AI workloads while integrating with your existing infrastructure. The key is finding tools that can grow with your AI initiatives rather than requiring you to replace everything as you scale.

Look for platforms that offer AI-specific features like automated model performance tracking, data drift detection, and bias monitoring. These capabilities should work out of the box rather than requiring extensive custom configuration. The best tools can automatically establish baselines for normal behavior and alert you when something changes, rather than forcing you to manually define every threshold.

Integration capabilities are equally important. Your AI monitoring solution needs to connect with your data warehouse or storage solution, data orchestration tools, and existing monitoring platforms. Tools that can automatically discover and monitor new AI components as you deploy them will save significant time and reduce the risk of monitoring gaps.

Consider solutions that can scale automatically as your AI footprint grows. Manual monitor creation and custom SQL test writing doesn’t scale when you’re dealing with dozens or hundreds of AI models and data pipelines. Look for platforms that can recommend new monitoring rules, automatically adjust thresholds based on changing conditions, and make it easy for non-technical team members to set up monitoring for their own AI tools.

Set up monitoring dashboards

Quality AI monitoring dashboards need to serve multiple audiences with different needs. Data scientists want detailed model performance metrics, operations teams need infrastructure health indicators, and business stakeholders want high-level summaries of AI application performance. The challenge is presenting all this information in ways that each group can understand and act upon.

The most successful monitoring setups can automatically determine what to monitor based on how your AI applications actually behave in production. Rather than guessing which thresholds to set, look for tools that can learn normal patterns and recommend appropriate alerts. This is especially important as AI applications can have complex seasonal patterns or gradually shifting baselines that are difficult to define manually.

Train teams and establish response protocols

Having great monitoring tools means nothing if your teams don’t know how to respond when alerts fire. AI incidents often require different response protocols than traditional application failures because the problems can be more subtle and the solutions less obvious.

Start by defining roles and responsibilities for different types of AI incidents. Data quality issues might require different expertise than model performance problems. Make sure everyone knows who to contact for different scenarios and establish clear escalation paths when initial responses don’t solve the problem.

Training should cover both the technical aspects of using your monitoring tools and the broader context of how AI applications can fail. Data contracts should be part of this training, helping teams understand who is responsible for maintaining specific data quality standards and what to do when those standards aren’t met. Help teams understand the difference between infrastructure problems that need immediate attention and gradual performance degradation that might require model retraining or data pipeline adjustments.

The impact of proper training and protocols is measurable. Organizations report 6,500 annual reclaimed data personnel hours when teams are properly trained on data plus AI observability tools and processes. As a Product Line Lead at a major pharmaceutical company noted, “Monte Carlo is a user-friendly tool that fits well with our whole data mesh approach where we don’t want to have an IT team in the critical path. Having this tool with the data product teams enables self-sufficiency.”

Create runbooks for common AI incident scenarios, but keep them practical and actionable. Include specific steps for diagnosing problems, temporary workarounds to minimize business impact, and criteria for deciding when to take AI applications offline. The goal is enabling teams to respond confidently even when facing unfamiliar AI-specific problems.

AI observability challenges and how to overcome them

Implementing AI observability sounds straightforward in theory, but organizations quickly discover that the reality is far more complex. The challenges go well past the technical aspects of monitoring AI applications and extend into organizational, operational, and ethical considerations that many teams aren’t prepared to handle.

These obstacles can derail data plus AI observability initiatives if you don’t anticipate them early. The good news is that other organizations have faced these same challenges and developed practical approaches for overcoming them. Understanding what you’re likely to encounter and having strategies ready can make the difference between a successful implementation and a stalled project.

Scaling monitoring across growing AI portfolios

The biggest challenge most organizations face is scale. What starts as monitoring a single AI application quickly becomes managing observability for dozens or hundreds of models, each with different data sources, performance characteristics, and business requirements. Traditional monitoring approaches that work for a few applications break down completely when you’re dealing with AI at enterprise scale.

The problem gets worse as AI adoption accelerates within organizations. New teams start building AI applications, existing applications get updated with new models, and the complexity of interconnected AI components grows exponentially. Manual approaches to setting up monitoring simply can’t keep pace with this growth.

How to overcome this challenge

Invest in monitoring platforms that can automatically discover and monitor new AI components as they’re deployed. Look for tools that can establish baseline performance metrics without manual configuration and recommend new monitoring rules based on observed patterns. Automation is essential because human teams can’t manually scale monitoring to match the pace of AI deployment.

Create standardized monitoring frameworks that teams can adopt consistently across different AI applications. Rather than letting each team build their own monitoring approach, establish organization-wide standards for how AI applications should be instrumented and monitored. This reduces the burden on individual teams while ensuring consistent coverage across your AI portfolio.

Focus on monitoring platforms that can aggregate information across multiple AI applications and present unified views of overall AI health. Individual dashboards for each application quickly become overwhelming, but consolidated views that highlight the most critical issues help teams prioritize their attention effectively.

Resolving AI incidents quickly and effectively

Even with excellent monitoring in place, AI incidents will occur. The second biggest challenge organizations face is resolving these incidents quickly when they do happen. AI problems are often more complex than traditional application failures because they can involve data quality issues, model performance degradation, or subtle biases that are difficult to diagnose and fix.

Resolution becomes particularly challenging because AI incidents often require expertise from multiple teams. A single problem might involve data engineers, data scientists, infrastructure specialists, and business stakeholders, each with different perspectives on what might be wrong and how to fix it.

The business impact of slow incident resolution can be severe. Organizations without AI observability face significant financial exposure, with documented cases of single incidents costing $1.5 million or more.

How to overcome this challenge

Develop clear incident response procedures that specify who needs to be involved for different types of AI problems. Create escalation paths that bring in the right expertise quickly rather than wasting time with teams that can’t actually solve the problem. Include temporary workarounds in your procedures so you can minimize business impact while working on permanent fixes.

Invest in monitoring tools that provide rich context when problems occur, not just alerts that something is wrong. The best data plus AI observability platforms can show you exactly what changed before an incident occurred, which data sources might be affected, and which other AI applications could be at risk. This context dramatically reduces the time needed to diagnose and resolve problems.

Build relationships between different technical teams before incidents occur. Regular cross-functional meetings where data scientists, engineers, and operations teams discuss potential AI risks help everyone understand how problems might manifest and who has the expertise to solve different types of issues.

Making observability tools accessible across diverse teams

AI observability tools are only valuable if teams actually use them effectively. Many organizations discover that their monitoring platforms work well for technical teams but are too complex for business users who also need visibility into AI performance. This creates gaps in monitoring coverage and reduces the overall effectiveness of data plus AI observability initiatives.

The challenge becomes more complex as AI adoption spreads throughout organizations. Marketing teams building recommendation engines, finance teams using forecasting models, and customer service teams deploying chatbots all need some level of AI monitoring capability, but they may not have the technical background to use traditional monitoring tools.

How to overcome this challenge

Invest in monitoring platforms that can automatically discover and monitor new AI components as they’re deployed. Modern data plus AI observability platforms like Monte Carlo can establish baseline performance metrics without manual configuration and recommend new monitoring rules based on observed patterns. Automation is essential because human teams can’t manually scale monitoring to match the pace of AI deployment.

Create standardized monitoring frameworks that teams can adopt consistently across different AI applications. Rather than letting each team build their own monitoring approach, establish organization-wide standards for how AI applications should be instrumented and monitored. This reduces the burden on individual teams while ensuring consistent coverage across your AI portfolio.

Focus on monitoring platforms that can aggregate information across multiple AI applications and present unified views of overall AI health. Individual dashboards for each application quickly become overwhelming, but consolidated views that highlight the most critical issues help teams prioritize their attention effectively.

Balancing transparency with data privacy

AI observability requires access to detailed information about how AI applications process data and make decisions. This creates tension with data privacy requirements, especially when dealing with sensitive personal information or proprietary business data. Organizations need visibility into AI behavior while ensuring they don’t compromise data security or violate privacy regulations.

The challenge is particularly acute when monitoring AI applications that process customer data, financial information, or healthcare records. Traditional monitoring approaches that log detailed request and response information may not be appropriate when dealing with sensitive data, but reducing visibility can make it difficult to detect problems or bias in AI behavior.

How to overcome this challenge

Implement monitoring approaches that provide the context you need without exposing sensitive data directly. Look for tools that can track AI performance patterns and detect anomalies without logging the actual data being processed. Advanced data plus AI observability platforms now include features like data masking and differential privacy that provide monitoring insights while protecting individual privacy.

Establish clear data governance policies that specify what information can be logged and monitored for different types of AI applications. Work with your legal and compliance teams to understand what monitoring data you can collect and retain, then design your data plus AI observability approach within those constraints. Monte Carlo and similar platforms offer built-in governance features that can help enforce these policies automatically.

Choose monitoring platforms that include strong data security features like encryption, access controls, and audit logging. Make sure your data plus AI observability tools meet the same security standards as your AI applications themselves, and regularly validate that monitoring data is being handled appropriately through automated compliance checks.

5 best practices for AI observability

Successfully implementing AI observability requires more than just deploying monitoring tools. The organizations that get the most value from their AI investments follow specific practices that ensure their monitoring efforts actually improve AI performance and reliability. These practices have been developed through real-world experience at companies that have successfully scaled AI operations.

The key is treating AI observability as an integral part of your AI development process, not an afterthought that gets added once applications are already in production. The most effective organizations embed monitoring considerations into every stage of their AI lifecycle, from initial development through ongoing operations.

Track end-to-end lineage and context

Understanding how data flows through your AI applications is essential for effective monitoring. In data fabric architectures where information flows across multiple platforms and data sources, this becomes even more complex. When an anomaly appears in a key performance indicator, you need to be able to trace back through your model to the specific dataset and feature pipeline that might be causing the problem. This end-to-end visibility is what separates effective AI monitoring from basic application monitoring.

Data problems often originate far upstream from where they become apparent. A gradual change in data quality might not affect your AI’s performance immediately, but it can slowly degrade accuracy over weeks or months. Only by tracking complete data lineage can you identify these subtle problems before they impact business outcomes.

Implement monitoring that connects data sources, transformation processes, model training, and final outputs into a unified view. When problems occur, this context dramatically reduces the time needed to identify root causes and implement fixes. Teams should be able to see not just what went wrong, but exactly where in the pipeline the problem originated.

Use automated anomaly detection and intelligent alerting

Manual threshold setting doesn’t scale when you’re monitoring dozens or hundreds of AI models, each with different performance characteristics and seasonal patterns. Machine learning-based anomaly detection can automatically identify when your AI applications are behaving differently from their normal patterns, even when those patterns are complex and constantly changing. This approach applies to model performance as well as infrastructure monitoring, whether you’re implementing sql anomaly detection for database performance issues or tracking API response times and resource utilization.

The key to successful automated monitoring is implementing intelligent alerts that consider both severity and context. Teams shouldn’t be bombarded with notifications about minor fluctuations, but they need immediate alerts when critical issues occur. Focus on alert quality rather than quantity. A single well-contextualized alert that explains what’s wrong, why it matters, and what might have caused the problem is far more valuable than dozens of generic notifications.

Foster cross-functional collaboration

AI observability requires coordination between teams that traditionally work in isolation. DevOps teams understand infrastructure health, data engineers know about pipeline reliability, and machine learning teams focus on model performance. Effective AI monitoring brings these perspectives together into a unified approach.

Establish shared service level agreements and key performance indicators that all teams understand and contribute to maintaining. When everyone has visibility into how their work affects overall AI performance, they can make better decisions about priorities and resource allocation. As industry experts recommend, improving collaboration between data scientists, engineers, and business leaders is essential for fostering trust in AI applications.

Create regular cross-functional meetings where different teams can discuss AI performance trends, share insights about potential problems, and coordinate responses to incidents. These collaborative practices help teams catch problems earlier and resolve them more effectively when they do occur.

Integrate governance and compliance monitoring

AI observability must be integrated into your broader data governance framework to ensure that monitoring practices meet regulatory requirements and organizational policies. This means maintaining detailed audit trails of data and model changes, monitoring for bias drift over time, and ensuring that AI applications continue to operate within defined ethical boundaries.

Governance monitoring becomes particularly important as AI applications handle more sensitive decisions about hiring, lending, healthcare, and other areas where fairness and transparency are critical. Your data plus AI observability platform should automatically track and report on compliance metrics, not just technical performance indicators.

Build continuous feedback loops

Effective data plus AI observability embeds monitoring throughout the entire machine learning lifecycle, from training through production deployment. This means monitoring both offline performance during model development and online performance once applications are serving real users. The goal is creating feedback loops that enable rapid adaptation when monitoring alerts indicate problems.

Establish processes for quickly updating or retraining models when monitoring indicates that performance is degrading. The organizations that get the most value from AI observability are those that can rapidly adapt their applications based on monitoring insights, rather than letting problems persist while they plan lengthy remediation projects.

Defining Terms: AI Agent Observability, LLM Observability, AgentOps and more

When any category evolves as rapidly as AI observability and the broader agent reliability ecosystem, it’s naturally for the terms to become a little…inconsistent. In the next couple paragraphs, we’ll define some of these terms and their nuances and how they relate to the AI observability category at large.

What is AI 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.

What is LLM Observability?

Terms like 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, while each of these terms might be used interchangeably, the LLM is only one component of an agent or application. And because “observability” can and should indicate coverage for an entire pipeline or ecosystem, it’s most accurate to refer to observability for AI systems as either AI observability or agent observability.

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.

Much like the AI industry at large, the lexicon for AI reliability tooling has evolved rapidly since 2023, but all of these categorical terms can be considered roughly synonymous. For a third-party opinion, consider Gartner’s “Innovation Insight: LLM Observability” which describes a similar definition of terms.

What is the best data and AI observability platform?

When you’re evaluating data plus AI observability platforms, you’re not just choosing monitoring tools. You’re selecting the foundation that will determine whether your AI initiatives succeed or fail at scale. Monte Carlo isn’t just another monitoring platform. We’re the only solution built specifically to handle the unique challenges that AI applications present in production environments.

We’ve spent years perfecting data plus AI observability for the world’s most demanding enterprises, and that experience gives us an unmatched understanding of how data flows through complex AI pipelines. While our competitors are still figuring out how to monitor basic AI applications, we’re already solving the hardest problems that organizations face when deploying AI at enterprise scale. Our platform handles the intricate dependencies between data quality, model performance, and infrastructure health that other tools miss entirely.

Our automated discovery and monitoring capabilities set us apart from every other solution in the market. While other platforms require your teams to manually configure monitoring for each AI component, Monte Carlo automatically maps your entire AI ecosystem and establishes intelligent monitoring baselines without any human intervention. This means you can deploy new AI applications knowing they’ll be monitored properly from day one, not weeks later after someone remembers to set up alerts.

When problems occur, and they will, Monte Carlo’s automated root cause analysis gets you to solutions faster than any other platform. Our data lineage tracking doesn’t just tell you something broke; it shows you exactly what caused the problem, which data sources are affected, and which other AI applications are at risk. This level of insight is impossible with traditional monitoring tools that were never designed for the complexities of AI applications.

Most importantly, Monte Carlo scales with your AI ambitions. Whether you’re monitoring your first AI application or your hundredth, our platform adapts automatically to provide the coverage you need without overwhelming your teams. We’ve built the only data plus AI observability solution that grows with you, supports diverse technical skill levels, and maintains the security and governance standards that enterprises require. When you choose Monte Carlo, you’re choosing the platform that will power your AI success for years to come.

AI observability doesn’t have to be complicated

AI observability has moved from being a nice-to-have feature to an essential requirement for any organization serious about deploying artificial intelligence at scale. As we’ve explored throughout this article, the challenges of monitoring AI applications go far past traditional software monitoring, requiring specialized approaches to handle data quality, model performance, infrastructure health, and governance requirements. The organizations that get this right will have a significant competitive advantage, while those that don’t will face mounting costs from AI failures, regulatory penalties, and lost customer trust.

The path forward doesn’t have to be overwhelming. By following the best practices outlined in this article and taking a methodical approach to implementation, organizations can build effective data plus AI observability programs that grow with their AI initiatives. The key is starting with your most critical applications, choosing tools that can scale automatically, and fostering the cross-functional collaboration needed to resolve complex AI incidents quickly. Success comes from treating AI observability as an integral part of your development process rather than an afterthought.

For organizations ready to implement world-class data plus AI observability, Monte Carlo offers the most advanced platform available today. Our automated discovery and monitoring capabilities, combined with years of experience solving data quality problems at enterprise scale, make us uniquely positioned to handle the complex challenges that AI applications present. When you choose Monte Carlo, you’re not just selecting an observability tool. You’re partnering with the platform that will enable data plus AI observability in your organization.

Our promise: we will show you the product.