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Observability helps teams see how an AI application produced an answer—not just the answer itself. By connecting model calls with retrieval, prompts, and tool operations, traces make it easier to investigate where a request slowed, failed, or went off course. Evaluations and repeatable experiments then help determine whether a change improved behavior.
What observability means for an AI application
AI observability is structured visibility into how an application executes and behaves. A trace links the operations involved in a request into a path that can be inspected. Individual spans or component-level records can help locate where a request took an unexpected turn.
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That view needs to extend beyond model-call logs when the application uses other components. The Arize LLM Observability Checklist describes traces as recording request paths across multiple steps and includes surrounding application context such as retrieval and external tools.
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In a simple model interaction, a team may be able to inspect the input and output directly. In an agent or chained application, the final response can depend on several model calls, retrieved context, tool choices, and intermediate decisions. The relevant clues may be distributed across those steps.
#1 Best Overall
Arize’s July 18, 2025 article on observability for AI agents and applications argues that multi-step and stateful systems can be more difficult to monitor and debug than single-turn calls. That is a vendor’s explanation of the challenge, not an independent measurement of how much harder these systems are.
Example: investigating a weak RAG answer
Suppose a retrieval-augmented generation (RAG) assistant gives a vague answer. A trace may let the team inspect whether the retrieved passages were relevant, whether the prompt included them as intended, what the model returned, and whether a later tool step changed the result. That visibility narrows the investigation; it does not automatically identify the root cause or prove which component is responsible.
Tracing and evaluation answer different questions
Tracing helps answer, “What happened during this request?” Evaluation asks whether outputs or retrieval results meet criteria the team has defined. A trace can expose a sequence of operations, but it does not by itself establish that an answer is accurate, useful, or safe.
Evaluation is most useful when criteria are concrete enough to assess. A team might define checks for whether retrieved material addresses the question or whether an answer follows a required format. Datasets provide examples to test against, while experiments make it possible to compare versions or changes systematically. Arize Phoenix documents tracing alongside evaluation, datasets, and experiments in its documentation; these are complementary practices, not substitutes for one another.
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How to start observing an AI workflow
- Choose a representative workflow. Start with one path that reflects how the application is used, rather than instrumenting every possible workflow at once.
- Instrument the end-to-end path. Include the relevant model calls, retrieval steps, and tool operations so the trace shows how the request moved through the application. The exact instrumentation depends on the framework and deployment.
- Decide what context is appropriate to capture. Choose what request and component information the team needs to investigate behavior, and account for the application’s data-handling requirements.
- Inspect traces for recurring failure patterns. Look for where requests slow down, fail, or produce unexpected intermediate results. Treat trace evidence as a way to guide investigation, not an automatic diagnosis.
- Define evaluations for assessable quality dimensions. Use representative examples and criteria that the team can apply consistently to outputs or retrieval results.
- Repeat evaluations after changes. Re-run the checks when changing prompts, models, or retrieval so the team can compare behavior against the same examples.
Phoenix documents OpenTelemetry and OpenInference use, as well as tracing and evaluation capabilities. AWS also documents OpenTelemetry integration for its managed AI observability offering. These sources establish that such capabilities are available, but implementation details vary by framework and deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose an observability approach
The available documentation points to different approaches, not an independently tested winner. Compare candidates against the team’s stack and operating requirements:
- Instrumentation coverage: Check compatibility with the frameworks and model providers in use, and whether retrieval and tool operations can be represented in the trace.
- Evaluation workflow: Determine whether tracing, evaluations, datasets, and experiments are available together or must be assembled separately.
- Deployment model: Decide whether open-source software that the team operates itself or a managed service better fits its capabilities and requirements. Phoenix documents open-source tracing and evaluation; AWS documents a managed offering through Amazon OpenSearch Service AI observability.
- Data and operating requirements: Verify data handling, retention, access controls, and operating costs directly for each candidate. Requirements depend on the application; there is no universal retention period, sampling rate, or alert threshold established for every system.
Vendor documentation describes supported capabilities; it is not an apples-to-apples performance comparison. Choose based on compatibility and operational needs, then validate that the resulting traces and evaluations answer the questions your team actually has.
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