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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteIf an agent’s useful result seems to disappear, compare it across the run: the tool’s actual response, the trace record, what the viewer displays, and the input to the next model step. The first point where those values differ identifies whether to investigate execution, trace capture or transformation, viewer rendering, or context assembly. A trace can show what was recorded; it does not, by itself, prove what the model received.
Start with the first missing transition
Do not begin with the final answer or assume the model ignored a result. Work forward from the tool or sub-agent expected to produce it, and compare adjacent stages. OpenAI describes its tracing dashboard as showing each step’s recorded inputs, outputs, duration, and status; its documented workflow is to open a session, expand a turn, and select a step in the timeline or event list. See the OpenAI tracing guide.
- Identify the run. Save the run or session ID and time window, then locate the corresponding project or session in your trace interface.
- Inspect the producing step. Open the relevant tool or agent step and note its input, output, duration, and status. Check whether the result is absent, shortened, or transformed in the recorded output.
- Compare the next step. Inspect the immediately following step, especially the actual request sent to the model. Determine whether the result was present in the trace but absent from that request.
- Compare against the tool’s own record. If available, check the tool response or application log for the same run. Use the same run ID, timestamp, and tool call to avoid comparing unrelated events.
Keep the smallest safe example that shows the mismatch, along with the step name, status, timestamps, framework and SDK versions, and relevant configuration. Comparing these boundaries is more informative than trying to infer what happened from the final response.
Use the mismatch to locate the likely layer
| What you find | Where to investigate | What to compare |
|---|---|---|
| The tool’s own response lacks the information. | Tool execution or its upstream data source. | The tool request and response, plus the tool’s status. |
| The tool response contains it, but the trace record does not. | Trace capture, output transformation, redaction, serialization, or storage. | The tool response against the recorded step output, and the client’s capture configuration. |
| The trace record contains it, but the viewer does not display it. | Viewer rendering, collapsed fields, display limits, or query selection. | The raw or exported trace record against the rendered panel. Viewer behavior varies; the documentation cited here does not establish a universal display rule. |
| The trace contains it, but the next model request does not. | Context assembly, token budgeting, truncation, or explicit prompt filtering. | The recorded output against the actual subsequent request. |
| It appears only in some runs. | Run-specific branching, retries, sampling, persistence, or configuration differences. | Complete traces and metadata for affected and unaffected runs, including versions and settings. |
These are diagnostic leads, not proof of a particular mechanism. In particular, intermittent results do not establish that a viewer pruned data or that persistence was asynchronous; verify the suspected behavior in the stack and version involved.
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Check trace capture and output filters
A trace can omit or alter a value even when the tool produced it. For example, the LangSmith Python Client reference documents hide_outputs: it can hide run outputs or accept a function that processes outputs when runs are created. The client reference documents equivalent input-hiding behavior through hide_inputs.
If the tool’s own response is complete but its recorded output is missing or reduced, inspect the configuration for the exact tracing client and version. Look for intentional hiding, redaction, output-processing hooks, and serialization or storage behavior. LangSmith’s option names and behavior are specific to that client; do not assume other observability products use the same settings or defaults.
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Check model context separately from trace visibility
A result visible in a trace is not necessarily present in a later model request. Inspect the actual request that followed the tool call and determine how the application assembled its context. A viewer’s presentation is not a substitute for checking that request.
OpenAI’s Realtime API reference documents one specific example: when a conversation exceeds the input limit, automatic truncation can omit older messages from model context. The reference also describes disabling truncation, which makes an overflow return an error, and a retention-ratio strategy. These are Realtime API behaviors, not universal agent rules; the reference’s token-limit example should not be treated as a general limit for other models or APIs.
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Keep the incident reproducible
Once you find the earliest mismatch, capture enough detail for someone to repeat the diagnosis without exposing unnecessary sensitive data.
- Run or session ID and the relevant time window.
- Exact tool or agent step name, status, and timestamps.
- Framework, tracing client, SDK, and viewer versions.
- Relevant capture, redaction, transformation, and context settings.
- The smallest safe tool response, trace record, and subsequent model request that demonstrate the difference.
Compare values at each boundary, and record which boundary first diverges. That prevents a trace-display issue from being mistaken for a tool failure, or a context omission from being mistaken for missing trace data.
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What the vendor examples do—and do not—establish
OpenAI’s tracing guide supports inspecting recorded step inputs, outputs, duration, and status in its dashboard. Its Realtime API reference documents a context-truncation mechanism for that API. The LangSmith client reference documents client-side input and output hiding or processing. Together, these examples show why capture, display, and model context should be checked separately; they do not establish a single pruning policy shared by all agent frameworks.
If you are evaluating an observability approach, compare whether your framework and tool calls are captured automatically or need instrumentation; whether raw run payloads can be inspected or exported; where inputs and outputs can be hidden or transformed; whether nested agents and tool calls can be correlated; and whether data retention and deployment meet your requirements. LangChain’s LangSmith overview describes its tracing and OpenTelemetry capabilities, but that product information is not an independent ranking or a recommendation for every deployment.
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