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Start by identifying which Gemini product is answering: the Gemini API, Gemini Enterprise Agent Platform/CX Agent Studio, or the consumer Gemini app. Their controls and diagnostic evidence differ. Then classify the failure—wrong facts, missing content, a blocked or truncated response, a tool-call problem, or an API error—before changing anything.
1. Identify the Gemini surface and capture the failure
Do not apply API settings to a consumer-app problem, or assume a custom instruction in the Gemini app is equivalent to an API agent’s system instruction, tool definition, or grounding configuration. First record:
- The exact prompt and complete response.
- Which Gemini surface, model, and API version are involved.
- Request parameters, enabled tools, and grounding or retrieval settings.
- Whether the result is factually wrong, unsupported, incomplete, blocked, truncated, repetitive, or interrupted by a tool or service error.
Separate a fluent but incorrect answer from a failed request. For API errors, preserve the actual status and error message; for agent workflows, note the turn and any tool call associated with the failure. Google’s Gemini API troubleshooting guide and Agent Platform troubleshooting documentation cover different layers of these problems.
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For a Gemini API agent, confirm that the selected model supports the capability you use, that the API version and parameter values are valid, and that the output allowance is sufficient for the requested response. Check the model reference for the model’s maximum output tokens; a request cannot produce more output than the model supports. The API guide lists general ranges of 1–8 candidates and 0.0–1.0 for temperature and top-p, but these details can change and model-specific behavior matters. Consult the current troubleshooting guide and model documentation for the model you actually call.
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Handle errors according to their cause
Do not retry every failed request. For transient service or rate-limit failures such as 429 or 503, use exponential backoff with jitter and a retry limit. Google’s API guide recommends exponential backoff for retryable errors. It says the Python SDK automatically retries transient errors up to four times, starting at about one second and increasing to a maximum delay of 60 seconds; SDK behavior can change by version. Check your installed SDK’s documentation rather than assuming those values apply to every client.
For client errors such as 400, 402, or 403, inspect the error cause and correct the request, permissions, or configuration instead of retrying indiscriminately. A retry does not repair an invalid request or missing authorization.
3. Determine whether safety or generation termination stopped the answer
If a prompt or completion is blocked, inspect the relevant safety settings and the content that triggered the filter. If the response ends early, distinguish a safety block from output-token truncation or an incomplete agent execution. Check the model’s output limit and the request’s configured allowance before rewriting the prompt.
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A RECITATION finish reason indicates that generated text may resemble certain data. Google’s troubleshooting guidance suggests making the prompt or context more distinctive and trying a higher temperature in this situation. Treat that as a targeted diagnostic, not a universal fix: the same guidance warns that changing Gemini 3.x generation parameters from their defaults can cause unexpected looping or degraded performance on complex reasoning tasks.
4. Fix unsupported, stale, or poorly grounded claims
If an answer depends on recent or obscure facts, enable an appropriate grounding option and verify the evidence it returns. Google recommends Search grounding for those cases; its prompt-strategies page says it connects the model to real-time web content and should be enabled when obscure or recent facts may be needed. For private organizational content, use a connected source suited to that data, such as Agent Search, rather than assuming public web search can retrieve it.
- No useful evidence returned: Check whether grounding or retrieval is enabled for the relevant request and whether the source contains the needed information.
- Evidence exists but is irrelevant or incomplete: Review source relevance, retrieval coverage, and how the retrieved material is placed in the model’s context. Grounding metadata may be absent when relevance is low or the response contains incomplete information.
- The answer makes claims beyond the evidence: Inspect grounding chunks and supports to see which answer segments have supporting material. Grounding is evidence to check, not a guarantee that every claim is correct.
- The task is arithmetic, counting, or other computation: Use code execution rather than relying on free-form text generation.
For API Search grounding, see Grounding with Google Search. For retrieval from connected data, see Agent Search and the grounding overview. The right setup depends on whether the answer needs public web information or private connected documents.
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Make multi-step instructions explicit
When the task is ambiguous or has several constraints, state the requested work and constraints directly. For long-context prompts, put the supporting context first and the specific question or instruction at the end. This makes it easier to distinguish a prompt-following failure from missing or poor-quality source data.
5. Trace tool use from selection to interpretation
A tool-related failure is not one problem. Follow the entire chain and identify the first point that went wrong:
- Selection: Did the agent choose the tool that matches the task?
- Arguments: Did it provide every required argument, with valid types and the intended meaning?
- Execution: Did the endpoint run successfully, or fail because of authentication, permissions, a timeout, or an internal error?
- Returned data: Did the tool return enough relevant information for the question?
- Interpretation: Did the model use the returned result accurately in its answer?
Agent Platform’s failure categories distinguish problems such as incorrect tool selection, missing or invalid parameters, omitted required calls, irrelevant or insufficient tool output, and tool execution failures. Keep these causes separate: rewriting a prompt will not fix a broken endpoint or bad permissions, and a model misreading valid results is different from a tool outage. See the Agent Platform troubleshooting guide.
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For client-side function calling with the Gemini API, the application—not the model—must execute the requested function and return the result and conversation parts so the model can continue. Check that this handoff is implemented and that the result returned to the model is complete. The function-calling guide describes the flow.
If the agent repeats tool calls, check that the endpoint is available and that relevant prior context is still being passed through the conversation. Google’s API troubleshooting guide identifies unavailable endpoints and lost context as possible causes; a concise instruction such as “Provide the answer once” may help with repeated responses, but it will not restore a failing endpoint.
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Missing requested content or early stopping
Write an evaluation expectation for the full answer and inspect the trace to find where execution stopped. Agent Platform identifies “Incomplete Execution” as a pattern in which an agent partially completes a task or stops prematurely. If the response is cut off at the output limit, increase the request allowance only within the model’s supported maximum.
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Repeated text, newlines, or tool calls
Google’s troubleshooting guide lists repetitive tokens, repeated newlines or duplicate text in structured output, repeated tool calls, and repetitive unstructured text as distinct symptoms. Check for conflicts between schema field ordering and the output schema, escaped Unicode or tab sequences in the prompt, an unavailable tool endpoint, or a request the model cannot resolve. Change generation parameters only as a targeted test; for complex reasoning with Gemini 3.x, the guide cautions against departing from defaults without a clear reason.
7. Evaluate, trace, make one targeted change, and retest
For a recurring issue, use representative failures rather than judging a single response in isolation. CX Agent Studio evaluation supports expectations for an agent message, a tool call, or an agent handoff. Follow this sequence:
- Build cases: Include the questions that fail, the expected answer content, and expected tool calls or results where relevant.
- Review results: Check summary metrics and individual case results, then group related failures into clusters.
- Open traces: Follow conversation inputs, calls, and results turn by turn to locate the failure.
- Classify the cause: Decide whether it is in the prompt, tool configuration or execution, or source-data quality.
- Change one thing: Make a targeted correction so you can tell what affected the outcome.
- Rerun the evaluation: Compare the new results with the prior run to see whether the recurring failure improved.
See CX Agent Studio evaluation documentation and the Agent Platform troubleshooting guide for evaluation and trace workflows.
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8. If the problem is in the consumer Gemini app
For the consumer Gemini app, check whether custom instructions are enabled for the signed-in account and supported surface. Google says the feature is available only in specified Gemini surfaces and is not available for certain features such as Gems. Its help page also cautions that instructions to forget or avoid topics do not always work perfectly. Those controls are different from API parameters, function calling, grounding configuration, and developer traces; use those diagnostics only if the setup actually includes developer-built components. See Google’s Gemini Apps custom-instructions help page.
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