Why observability is essential for AI agents

As excitement around artificial intelligence (AI) continues to sweep the business world, attention is turning to the technology’s newest iteration: AI agents.

Unlike traditional AI models, AI agents can make decisions without constant human oversight. They work autonomously to achieve complex goals such as answering customer questions, optimizing a supply chain or analyzing healthcare data to provide a diagnosis.

In practice, this means that AI agents can handle entire workflows from start to finish—such as automatically processing insurance claims or managing inventory levels—rather than just providing recommendations.

Recent estimates show organizations rapidly adopting AI agents. A KPMG survey found that 88% of organizations are either exploring or actively piloting AI agent initiatives.¹ Gartner predicts that by 2028 more than a third of enterprise software applications will include agentic AI—the underlying technology that enables AI agents.²

However, the very capabilities that make AI agents so valuable can also make them difficult to monitor, understand and control.

AI agents use large language models (LLMs) to reason, create workflows and break down tasks into subtasks. They access external tools—such as databases, search engines and calculators—and use memory to recall previous conversations and task results.

While this process enables them to work independently, it also makes them far less transparent than traditional applications built on explicit, predefined rules and logic.

This inherent complexity and lack of transparency can make it difficult to trace how AI agents generate specific outputs. For organizations, this can pose serious risks, including:

• Compliance violations: When agents handle sensitive data, organizations cannot demonstrate decision-making processes or prove regulatory adherence.
• Operational failures: Without visibility into agent reasoning, teams can struggle to identify root causes or prevent recurring errors.
• Trust erosion: Unexplainable agent actions can damage stakeholder confidence, particularly when agents make critical business decisions or interact directly with customers.

To mitigate these risks, organizations increasingly turn to AI agent observability to gain insight into the behavior and performance of AI agents.

AI agent observability is the process of monitoring and understanding the end-to-end behaviors of an agentic ecosystem, including any interactions that the AI agent may have with large language models and external tools.

It comes from the larger practice of observability, which is the ability to understand a system's internal state by analyzing its telemetry data—that is, its external outputs, such as metrics, events, logs and traces, commonly known as “MELT data.”  

With AI agent observability, organizations can evaluate agent performance by collecting data about actions, decisions and resource usage. It helps answer critical questions, such as:

• Is the agent providing accurate and helpful answers?
• Is the agent using processing power efficiently?
• Is the agent using appropriate tools to fulfill its goals?
• What are the root causes of issues with an agent?
• Is the agent complying with AI ethics and data protection mandates?

With these insights, organizations can troubleshoot and debug issues more effectively and improve the performance and reliability of AI agents. 

Multi-agent systems use multiple AI agents that work together to complete complex tasks, such as automating an enterprise sales pipeline or answering questions and generating tickets for an IT support system.

Unlike single-agent systems where failures can often be traced to a specific component, multi-agent systems are much more complex. With so many interactions between autonomous AI agents, there is a greater potential for unpredictable behavior.

AI agent observability provides critical insight into these multi-agent systems. It helps developers identify the specific agent or interaction responsible for an issue and provides visibility into the complex workflows that the agents create. It also helps identify collective behaviors and patterns that could escalate and cause future problems.

For example, in a multi-agent travel booking system with separate agents for flights, hotels and car rentals, a booking might fail at any point. Observability tools can trace the entire end-to-end process to identify exactly where and why the failure occurred.  

Many organizations use open-source solutions such as IBM BeeAI, LangChain, LangGraph and AutoGen to build multi-agent systems faster and more safely. These solutions provide a software development kit (SDK) with tools for creating AI agents and an agentic AI framework—the engine that runs and coordinates agents.

AI agent observability works by collecting and analyzing telemetry data that captures both traditional system metrics and AI-specific behaviors. Teams can then use this data to understand agent decisions, troubleshoot issues and optimize performance.

AI agent observability uses the same telemetry data as traditional observability solutions but also includes additional data points unique to generative AI systems—such as token usage, tool interactions and agent decision paths. These AI-specific signals still fit within MELT (metrics, events, logs, traces). 

In addition to traditional performance metrics collected by standard observability tools—such as the utilization of CPU, memory and network resources—AI agent observability measures:

Tokens are the units of text AI models process—typically words or parts of words. Since AI providers charge by token usage, tracking this metric directly impacts costs. Organizations can optimize spending by monitoring token consumption. For instance, if certain customer questions use 10 times more tokens than others, teams can redesign how agents handle those requests to reduce costs.

As real-world data evolves, AI models can become less accurate over time. Monitoring key metrics of model drift—such as changes in response patterns or variations in output quality—can help organizations detect it early. For instance, a fraud detection agent might become less effective as criminals develop new tactics. Observability flags this decline so teams can retrain the model with updated datasets.

This metric measures the quality of an AI agent’s output and whether its answers are accurate, relevant and helpful. It tracks how frequently agents hallucinate or provide inaccurate information. It can help organizations maintain service quality and identify areas for improvement. For instance, if agents struggle with technical questions, teams can expand the agent's knowledge base or add specialized tools.

This measures how long an AI agent takes to respond to requests. Fast response times are critical for user satisfaction and business outcomes. For example, if a shopping assistant takes too long to recommend products, customers might leave without buying. Tracking latency helps teams identify slowdowns and fix performance issues before they impact sales.

Events are the significant actions that the AI agent takes to complete a task. This data provides insight into the agent’s behavior and decision-making process to help troubleshoot issues and improve performance.

Examples of AI agent events include:

When an AI agent uses an application programming interface (API) to interact with an external tool such as a search engine, database or translation service. Tracking API calls helps organizations monitor tool usage and identify inefficiencies. For instance, if an agent makes 50 API calls for a task that should need only 2-3, teams can fix the logic.

When AI agents use large language models to understand requests, make decisions or generate responses. Monitoring LLM calls helps reveal the behavior, performance and reliability of the models that drive the actions of AI agents. For example, if a banking AI agent gives a customer incorrect account information, teams can analyze the agent’s LLM calls to find the issue, such as outdated data or unclear prompts.

When an agent tries to use a tool but it doesn’t work, such as when an API call fails because of a network issue or incorrect request. Tracking these failures can improve agent reliability and optimize resources. For example, if a support agent can't check order status due to failed database calls, teams are immediately alerted to fix issues like missing credentials or service outages.

When AI agents escalate requests they can’t handle to human staff. This information can reveal gaps in agent capabilities and the nuances of customer interactions. For example, if a financial service AI agent frequently escalates questions to a human, it might require better financial training data or a specialized investment tool.

When something goes wrong—such as slow response times, unauthorized data access or low system resources—and the AI agent receives an automated warning. Alerts can help teams catch and fix problems in real time before they impact users. For example, an alert about high memory usage lets teams add resources before the agent crashes.

Logs are the detailed, chronological records of every event and action that occurs during an AI agent’s operation. They can be used to create a high-fidelity, millisecond-by-millisecond record of every event, complete with surrounding context.

Examples of logs in AI agent observability include:

These logs document every interaction between users and AI agents—including queries, intent interpretation and outputs. Organizations can use these logs to understand user needs and agent performance. For instance, if users repeatedly rephrase the same question, the agent likely doesn’t understand their intent.

These capture every exchange between agents and LLMs, including prompts, responses, metadata, time stamps and token usage. This data reveals how AI agents interpret requests and generate answers, including when the agent might be misinterpreting context. For example, if a content moderation AI agent wrongly flags benign content while missing harmful ones, these logs can expose the flawed patterns causing the mistakes.

These record which tools agents use, when they use them, what commands they send and what results they get back. This helps trace performance issues and tool errors back to their source. For example, if a technical support AI agent responds slowly to certain questions, logs might reveal it’s using vague search queries. Teams can then write more specific prompts to improve responses.

These logs record how an AI agent arrived at a decision or specific action when available—such as chosen actions, scores, tool selections and prompts/outputs—without implying access to hidden reasoning. This data is crucial for catching bias and ensuring responsible AI, especially as agents become more autonomous.

For example, if a loan AI agent unfairly rejects applications from certain neighborhoods, decision-making logs can help reveal discriminatory patterns in the training data. Teams then retrain the AI model to meet fair lending requirements.

Traces record the end-to-end “journey” of every user request, including all interactions with LLMs and tools along the way.

For example, the trace for a simple AI agent request might capture these steps.

• The user input that triggers the agent
• The agent's plan and task breakdown
• Any external tool calls (for example a web search)
• The LLM's processing of the request
• The prompt processing and response generation
• The response returned to the user

Developers can then use this data to pinpoint the source of bottlenecks or failures, and measure performance at each step of the process.

For instance, if traces show that web searches take 5 seconds while all other steps complete in milliseconds, teams can implement caching or use faster search tools to improve overall response time.

There are two common approaches for collecting data used in AI agent observability: built-in instrumentation and third-party solutions.

In the first approach, MELT data is collected through the built-in instrumentation of an AI agentic framework. These native monitoring and logging capabilities automatically capture and transmit telemetry data on metrics, events, logs and traces.

Many large enterprises and those with specialized needs adopt this approach because it offers deep customization and fine-grained control over data collection and monitoring. However, it also requires significant development effort, time and ongoing maintenance.

In the second approach, AI agent observability solutions provide specialized tools and platforms to gather and analyze MELT data. These solutions offer organizations rapid, simple deployment with pre-built features and integrations that reduce the need for in-house expertise. However, relying on a third-party solution can create dependence on a specific vendor and limit customization options to meet an organization’s highly specific or niche needs.

Some organizations opt to combine built-in instrumentation and third-party solution providers to collect AI agent telemetry data.

Both approaches typically rely on OpenTelemetry (OTel), an open-source observability tool hosted on the GitHub web-based platform.

OTel has emerged as the industry standard framework for collecting and transmitting telemetry data because it offers a vendor-neutral approach to observability that's particularly valuable in complex AI systems, where components from different vendors must work together seamlessly. It helps ensure that observability data flows consistently across agents, multiple models, external tools and retrieval augmented generation (RAG) systems.

Once organizations collect MELT data through their chosen approach, they can use it in several ways.

Some of the most common use cases include:

Teams use dashboards to view real-time metrics, event streams and trace maps. This consolidated view helps identify patterns and anomalies across the entire AI agent ecosystem. For example, a dashboard might reveal that customer service agents slow down every afternoon at 3 PM, prompting teams to investigate the cause.

When issues arise, teams correlate data across metrics, events, logs and traces to pinpoint exact failure points. For instance, linking a spike in error rates (metric) with specific API failures (events) and reviewing the decision logs helps teams understand why an agent behaved unexpectedly.

Organizations use observability data insights to improve agent efficiency. They might reduce token usage, optimize tool selection or restructure agent workflows based on trace analysis. For instance, they might discover that an agent searches the same database three times instead of saving the result after the first search.

Teams establish feedback loops where observability insights drive agent refinements. Regular reviews of MELT data help identify recurring issues and edge cases—such as agents struggling with refund requests or failing when users ask questions not covered in the documentation. These issues may signal the need for expanded training datasets and updated docs.

Consider how an online retailer might use observability to identify and correct an issue with an AI agent that interacts with customers.

First, the observability dashboard shows a spike in negative customer feedback for a particular AI agent.

When teams examine the agent’s logs, they discover it uses a database tool call to answer customer questions. However, the answers contain outdated or incorrect information.

A trace—the complete record of the agent’s step-by-step process for handling the customer question—pinpoints the specific tool call that returned the obsolete data. Further analysis reveals the precise dataset within the database that contains the outdated information.

With this insight, the online retailer updates or removes the faulty dataset. The team also updates the agent’s logic to validate data accuracy before responding to customers. As a result, the agent now provides accurate, helpful answers that improve customer satisfaction.

Although the majority of AI agent observability still involves handing alerts and anomalies off to team members for manual investigation and resolution, AI-powered automation is increasingly transforming how organizations collect, analyze and act on telemetry data.

Advanced observability solutions are now using these technologies to monitor, debug and optimize AI agents with little to no human intervention. Emerging use cases in this area include:

• Automatically collecting, processing and storing AI agent telemetry data for compliance audits and performance analysis
• Analyzing vast amounts of AI agent data to flag anomalies and identify issues
• Predicting problems with AI applications and agents before they occur
• Forecasting resource requirements based on usage patterns
• Suggesting improvements to logic or tool usage to optimize performance
• Preventing AI agents from accessing or sharing sensitive data