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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, tools, and other workflow steps, teams can investigate where a request went wrong. Evaluation then helps determine whether a change improved behavior against defined criteria.
What observability means for an AI application
Here, observability is structured visibility into how an application executes and behaves. A trace connects the operations involved in a request; spans or other component-level records let a team inspect individual steps. That can help locate where a request slowed down, failed, or produced an unexpected result.
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Useful context can extend beyond model calls to include retrieval and external tools. Arize’s 2023 LLM Observability Checklist describes traces as records of request paths across multiple steps and highlights telemetry about both LLM execution and surrounding application context.
Why observability matters more as workflows grow
A single model call may be relatively straightforward to inspect. A chained or agent-style application can involve several calls, retrieved context, tool choices, and state carried between steps. The final answer alone may not show where a problem began.
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In a hypothetical RAG assistant, for example, a weak answer might stem from irrelevant retrieved passages, prompt construction, the model’s response, or a later tool operation. A trace can help a team inspect that path; it does not automatically identify the root cause. Arize’s vendor-authored 2025 article on LLM observability for agents and applications explains why multi-step and stateful systems can be harder to monitor and debug. Treat that as the vendor’s framing, not as an independently measured result.
What tracing can show—and what evaluation must answer
Tracing helps answer “What happened in this request?” It gives the team a connected view of operations and their component behavior, which supports diagnosis.
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Evaluation helps answer “Was the behavior good enough?” A team defines criteria for outputs or retrieved results, then applies them to examples. Datasets and repeatable experiments help compare versions or changes. Traces and evaluations are complementary: a trace can expose a failure path, while evaluation can test whether behavior meets the team’s chosen standards.
Arize Phoenix documentation describes tracing alongside evaluation, datasets, and experiments. These are documented capabilities, not evidence that any tool guarantees answer quality or safety.
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How to begin with a small workflow
- Choose a representative path. Start with one workflow that reflects real application behavior, rather than instrumenting every process at once.
- Trace the end-to-end request. Include relevant model calls, retrieval, and tool operations so the path can be inspected across components.
- Decide what context to capture. Set limits based on your data-handling, access, and operational requirements; not every input or output is necessarily appropriate to record.
- Inspect traces for failure patterns. Look for where requests slow down, fail, or diverge from expected behavior. Use the trace as evidence for investigation, not as an automatic diagnosis.
- Define evaluations for assessable quality dimensions. Build a set of representative examples and criteria the team can apply to outputs or retrieval results.
- Repeat evaluations after changes. When prompts, models, or retrieval methods change, compare results against the same examples where possible.
Phoenix documentation and AWS documentation for AI observability in Amazon OpenSearch Service describe instrumentation and tracing capabilities. The implementation details depend on the application’s framework, providers, and deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess observability approaches
There is no universal winner established by the available vendor documentation. Use your own stack and operating requirements to compare options:
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- Instrumentation coverage: Check compatibility with your frameworks and model providers, and whether retrieval and tool operations can be represented in the trace.
- Evaluation workflow: Determine whether tracing, evaluations, datasets, and experiments are included or need to be assembled separately.
- Deployment model: Decide whether open-source, self-managed software or a managed service better fits your team’s operational needs. Phoenix documents open-source tracing and evaluation functions and use of OpenTelemetry/OpenInference; AWS documents a managed OpenSearch AI observability offering with OpenTelemetry integration.
- Data and operating requirements: Verify data handling, retention, access controls, and operating costs directly for each candidate. The cited documentation does not establish universal retention periods, sampling rates, or alert thresholds.
These descriptions can help build a shortlist, but they are not an apples-to-apples performance comparison. Collecting telemetry also does not, on its own, establish that an application is reliable, safe, or improving.
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