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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsRate-limit handling answers one question: did this request get through? It says nothing about whether a tool did its job, whether a retry repeated a side effect, or whether the user’s task was finished. If your agent runs now end with a clean status and a confident final message, but a record is missing, a file is half-written, or an action happened twice, the problem has moved past throttling. This guide gives a sequence for finding it: classify the error, inspect the run and its side effects, trace the model-to-tool path, and add checks tied to the intended result.
Why a fixed 429 can still leave a broken run
Backoff and retry logic protect the transport layer. An agent run is a longer chain: model calls, tool calls, possibly handoffs to other agents, and saved state. A run can survive every 429 and still fail at a step the retry code never sees. The model may summarize a tool error as if it were a success. A tool may return an empty result that looks valid. A retried turn may repeat work that already happened.
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The evidence from the vendors’ own documentation supports a narrow, practical claim: recorded activity and status are useful, but they are not proof the task was done correctly. OpenAI’s error and recovery guidance says it directly: “Inspect tool results even when a turn completes.”
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Step 1: Classify the error before retrying anything
A 429 is not always a temporary throttle. OpenAI’s support guidance separates temporary rate limits from exhausted credit or usage limits. Retrying the second kind only burns time. When you log a failure, capture:
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- the HTTP status;
- the error type, code and message;
- the request ID;
- which rate or usage limit was referenced, if the response names one.
If your code treats every 429 identically, a billing or usage-cap problem looks like a transient glitch, and your agent may keep trying or give up in a way that is hard to distinguish from normal completion.
Step 2: Bound the retries, and count the ones you didn’t write
For a genuinely temporary limit, OpenAI’s guidance is:
- Honor
Retry-Afterwhen it is present and valid. - If it is missing or invalid, use exponential backoff with jitter.
- Cap the number of attempts and the total retry time.
Two further points matter for quiet failures. First, eligible OpenAI SDK requests may already be retried inside the SDK, so wrapping them in your own loop multiplies attempts and delays in ways your logs may not show. Decide which layer owns retries. Second, OpenAI’s recovery instructions say: “Stop automatic retries if the error changes or the retry limit is reached.” If the first failure was a rate limit and the second is something else, a blind loop is now hiding a different problem.
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Step 3: Check before you replay
A retry is not evidence that the earlier attempt did nothing. A turn can fail after a tool already wrote a file, sent a message or created a record. OpenAI’s agent recovery procedure tells you to check the session, the turn and the saved items, and to confirm which actions were completed before repeating work.
In practice, before re-running a task, ask:
- Did any tool call in the failed attempt complete?
- Were results saved to the session, and are they intact?
- Does the external system already show the change (file, row, ticket, message)?
- If the action must be repeated, is it safe to do twice? If not, make it idempotent or skip it.
Step 4: Trace the whole path, not just the final answer
A clean final response can hide the step that failed. OpenAI’s tracing documentation describes recording model responses, tool calls, delegated work, duration, status and inputs and outputs, and its agent observability material describes following events and saved history. Google Cloud’s agent observability guidance frames the useful signals as model interactions, tool usage, latency, resource use and error rates, and notes that agent systems can drift or fail differently from conventional software.
What to look for in a trace
- A tool call with an error status or an empty or truncated output that the model then ignored.
- A handoff to another agent that returned nothing useful.
- Unusually short or long durations on a single step.
- Repeated calls to the same tool, which can indicate a retry that replayed a side effect.
- A final message that claims more than the recorded tool outputs support.
Treat a trace as evidence of what happened, not a verdict on whether it was right. The sources describe recorded activity and status; they do not claim a trace alone proves the task was completed correctly.
Step 5: Verify the outcome the user wanted
This step is practical guidance rather than a documented universal method. Add a check that tests the real result, independent of what the agent said:
- If the task was to create a record, query for it.
- If it was to produce a file or report, confirm it exists, is non-empty and has the expected structure.
- If it was to change state, read the state back.
- If it was to answer from retrieved data, confirm the retrieval step actually returned data.
Make a failed check a visible failure: mark the run as failed or needing review, rather than letting the model’s fluent summary stand. Keep the check’s result alongside the trace so you can correlate them later.
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Step 6: Instrument the work your tooling doesn’t cover
OpenTelemetry’s GenAI semantic conventions describe spans for agent invocation and tool execution, error information, and coverage of retries within one logical model operation. They also encourage manual instrumentation for tool execution, which automatic instrumentation does not reliably cover. If your tools call databases, browsers or internal services, wrap them in spans that record success or failure and the error.
These conventions are marked as Development. Names and fields may change, so check the current status of the specification before you standardize dashboards or alerts on specific attributes. No source reviewed gives a prevalence figure for quiet agent failures after rate-limit fixes, so there is no benchmark to compare yourself against; your own failed-outcome-check rate is the number to track.
Choosing an observability approach
The official sources support tracing and error inspection as capabilities but do not rank vendors. If you are choosing between options, compare them on these points:
Quick Recap
| Question | Why it matters |
|---|---|
| Does it record the full agent-to-tool path? | Gaps at tool boundaries are where quiet failures hide. |
| How does it expose errors, retries and side effects? | You need to see repeated or partial actions, not just final status. |
| Does it support your framework and deployment? | Manual instrumentation fills gaps but costs effort. |
| Can traces be tied to saved outputs and outcome checks? | A trace plus a result check is stronger than either alone. |
| What are its data sensitivity and retention controls? | Traces can contain prompts, tool inputs and outputs. |
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