What is AI observability?

Traditional observability tools understand the internal state or condition of a complex system using the three pillars of observability: logs, traces and metrics. AI applications and AI agents introduce an additional layer of complexity that requires unique observability tools, which can optimize model performance by monitoring AI-specific outputs and producing (often AI-generated) visualizations.

Unlike traditional software, the outputs of large language models (LLMs) and other generative artificial intelligence applications are probabilistic. Identical inputs can yield different responses, which can make it difficult to trace how inputs shape outputs, causing problems for conventional observability tools. Therefore, troubleshooting, debugging and performance monitoring are more complex in generative AI systems.

Additionally, explainability in AI—a growing field that seeks to reduce the “black box” qualities of many AI models—cannot yet fully explain how models interact with broader IT systems and workflows. AI observability solutions must prioritize things that they can effectively measure and analyze. This prioritization is especially important when using models from third parties such as OpenAI or Google, which execute and manage the models privately.

AI agents—systems that work autonomously to design and execute workflows across the IT ecosystem—pose their own unique challenges for observability and require an accordingly unique approach to data collection. Nearly half of executives surveyed in 2025 by the IBM Institute for Business Value cited “a lack of visibility into agent decision-making processes as a significant implementation barrier” for agentic AI. Observability for these systems is crucial for adoption.

AI observability works by collecting AI-specific metrics from across the tech stack in real time and organizing them into a dashboard.. AI observability gives administrators better insight into AI models’ inner workings while reducing bottlenecks, hallucinations and latency, among other common issues.

Observability platforms focus on three main types of telemetry, which have their own variations specific to AI systems: logs, traces and metrics.

Logs are granular, time-stamped, complete and immutable records of application events. Among other things, logs can be used to create a high-fidelity, millisecond-by-millisecond record of every event in an IT system, complete with surrounding context. In AI development, developers use logs for troubleshooting and debugging.

Traces record the end-to-end “journey” of every user request, from the user interface or mobile app, through the entire architecture and AI model, and back to the user.

Metrics are fundamental measures of application and system health over time. For example, metrics are used to measure how much memory or CPU capacity an application uses in five minutes, or how much latency an application experiences during a usage spike.

AI agents, generative AI and other AI systems like large language models also produce their own unique types of telemetry data that must be collected and analyzed to give administrators actionable insights.

Token usage, model drift and response quality are three core datapoints for most AI observability initatives. Monitoring these factors can help reduce performance issues over a model’s lifecycle and improve user experience.

More traditional telemetry data such as GPU usage, bottlenecks in network infrastructure and user interface feedback can also be collected by AI observability tools where they are relevant to the model.

A token is an individual unit of language—usually a word or a part of a word—that an AI model can understand. The number of tokens a model processes to understand an input or produce an output directly impacts the cost and performance of an LLM-based application. Higher token consumption can increase operational expenses and response latency.

Key metrics for tracking token usage include:

• Token consumption rates and costs, which can help quantify operational expenses.
  
  
• Token efficiency, a measure of how effectively each token is used in an interaction. Efficient interactions produce high-quality outputs while minimizing the number of tokens consumed.
  
  
• Token usage patterns across different prompt types, which can help identify resource-intensive uses of models.

These metrics can help organizations identify optimization opportunities for reducing token consumption, such as by refining prompts to convey more information in fewer tokens. By optimizing token utilization, organizations can maintain high response quality while potentially reducing inference costs for machine learning workloads.

Unlike traditional software, AI models are at risk of gradually changing their behavior in undesirable ways as real-world data evolves. This phenomenon, known as model drift, can significantly degrade AI system reliability and performance.

Key metrics for tracking model drift include:

• Changes in response patterns over time to identify emerging inconsistencies.
  
  
• Variations in output quality or relevance that might indicate declining model performance.
  
  
• Shifts in latency or resource utilization that can signal computational inefficiencies.

Drift detection mechanisms can provide early warnings when a model’s accuracy decreases for specific use cases, enabling teams to intervene before the model disrupts business operations.

Monitoring AI output quality is essential for maintaining trust, reliability and compliance. Key metrics for tracking response quality include:

• Hallucination frequency across different prompt types to identify possible triggers for inaccurate outputs.
  
  
• Factual accuracy of generated responses, though this metric often requires external validation and human oversight.
  
  
• Consistency of outputs for similar inputs to verify model stability over time.
  
  
• Relevance of responses to user prompts to assess how the model aligns with user intent.
  
  
• Latency tracking is critical for user-facing AI applications, where speed and accuracy often require tradeoffs. Monitoring response times across different prompt types can help organizations pinpoint performance bottlenecks and computational inefficiencies.

OpenTelemetry (OTel), an open source framework for collecting and transmitting telemetry data, can help ameliorate the observability challenges posed by AI.

For AI providers, OpenTelemetry offers a way to standardize how they share performance data without exposing proprietary model details or source code. For enterprises, it ensures that observability data flows consistently across complex AI pipelines that might include multiple models, various dependencies and retrieval augmented generation (RAG) systems.

Key benefits of OpenTelemetry for AI observability include:

• Vendor independence: Organizations can avoid lock-in to specific observability platforms, helping them to maintain flexibility as AI technologies evolve.
  
  
• End-to-end visibility: Telemetry data flows consistently from all components of AI application infrastructure.
  
  
• Future-proofing: As AI technologies evolve, the OpenTelemetry standard can adapt alongside them, helping to ensure observability strategies remain relevant.
  
  
• Ecosystem integration: Open standards enable observability across multivendor AI solutions and hybrid deployment models.
  
  
• Metadata standardization: Because OpenTelemetry is vendor-neutral, it enables developers to capture essential AI-related metadata and maintain visibility across the stack regardless of which observability backends or vendors they use.

Because of their high level of autonomy and automation within the network, AI agents require special attention from observability platforms—specifically regarding the actions they take within the system and those actions’ attendant logs and traces.

The very capabilities that make AI agents valuable—their use of LLMs, their recall of previous conversations and use of external tools—can make them difficult to monitor, understand and control.

Common actions an AI agent might take include calling an application programming interface (API) to interact with a search engine, calling an LLM to produce text or understand user input, escalating requests to human staff or passing along an automated warning about a security breach or low compute availability.

While these capabilities enable agents to work independently, they also make them far less transparent than traditional applications built on explicit, predefined rules and logic. By tracking the data associated with these agentic processes, administrators can gain insight into the agent’s behavior and help prevent compliance violations, operational failures and the subsequent erosion of user trust.

Unique logs for AI agent observability include:

• User interaction logs, which document every interaction between users and AI agents.
  
  
• LLM interaction logs, which document the interactions between the agents and LLMs.
  
  
• Tool execution logs, which measure which tools and instrumentation agents use, when they use them, what commands they send and what results they get back.
  
  
• Agent decision logs, which record how an AI agent arrived at a decision or specific action when available.

Benefits of AI observability include controlling the cost of model usage, ease of compliance, and improvement of the model itself.

Gaining greater insight into how AI-powered tools interact with the IT ecosystem can identify wasted resources. For example, if an organization finds that a model is using only a small portion of the processing power afforded to it, DevOps teams can downsize or divert resources to where they’re needed more.

AI observability tools can automatically collect, process and store AI agent telemetry data for compliance audits. Audit support is increasingly important as laws including the European Union’s AI Act, as well as various proposed state regulations in the United States, have prompted a wave of efforts to standardize the business use of AI and protect any personally identifiable information (PII) used by models.

For model developers, the volume of telemetry collected by observability tools can help reduce model drift, track which features are most valuable and useful (or most dysfunctional) and check for bias and fairness.

While AI observability is the practice of understanding AI systems through their telemetry data, machine learning observability is more specifically focused on how an AI tool uses data at the model level.

Understanding machine learning telemetry is crucial because the data a model ingests and learns from is constantly changing. Drilling down to understand how a model functions internally can teach administrators not just that the model has drifted or become less effective, but why.

Key metrics for machine learning observability, and often LLM observability, measure model quality, the data itself that the model ingests and how the model interacts with its surrounding infrastructure.

Different types of models have different quality metrics that an observability platform might track.

For a classification model, which sorts data into predefined classes, common performance metrics include:

• Accuracy: The number of correct predictions compared to the total number of predictions made
  
  
• Precision and recall: A measure of how relevant the selected items are
  
  
• F1 score: A commonly used combination of these metrics

A regression model, on the other hand, is often evaluated by metrics such as the root mean squared error, which measures the average difference between predictions made by the model and the actual data points in question.

Machine learning observability can measure data drift—such as large divergences between input data and training data, or a change in the statistical distribution of its predictions—as well as simple data quality metrics such as missing values and erroneous and invalid value types. Using these metrics, machine learning observability tools can perform a root cause analysis to understand model drift or prevent it before it happens.

Machine learning observability often also measures more traditional metrics that might be relevant to the model, such as latency, memory usage and throughput, or the number of predictions a model can make in any given amount of time.