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Freedom251 › Blog › AI agents

How to Troubleshoot a Gemini Agent That Gives Incorrect or Incomplete Answers

By Freedom251 Team · · 6 min read

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Start by identifying which Gemini product you are troubleshooting, then classify what went wrong: an API error, a blocked or truncated response, an unsupported answer, an incomplete task, or a tool failure. Gemini API agents, Gemini Enterprise Agent Platform/CX Agent Studio agents, and the consumer Gemini app expose different controls, so a fix for one surface may not apply to another.

First identify the Gemini surface and the failure

Record the exact prompt and response, model and API version, generation parameters, enabled tools, and any error or trace details. Note whether the answer is factually wrong, missing requested parts, blocked, cut off, repeating itself, or failing during a tool call. A fluent but incorrect answer is a different problem from a request that returns an API error.

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Then identify the product involved:

  • Gemini API: inspect the request, model support, parameters, grounding configuration, function calls, and application handling.
  • Gemini Enterprise Agent Platform or CX Agent Studio: use evaluations, failure categories, and traces to inspect agent execution.
  • Consumer Gemini app: check the relevant app feature and account or surface support. API parameters, developer tool calls, and agent traces do not apply unless your setup actually uses them.

For consumer-app custom instructions, see Google’s Gemini Apps custom instructions help. Google notes that the feature is available only on specified Gemini surfaces and is unavailable for some features, including Gems; instructions to forget or avoid topics may not work perfectly.

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Check the request and model configuration

For an API agent, verify that the selected model supports the feature you are using, that the API version is correct, and that parameter values are valid for that model. Google’s Gemini API troubleshooting guide lists general parameter ranges of 1–8 candidates, temperature 0.0–1.0, and top-p 0.0–1.0; these values and model limits can change, so check the current model reference for the specific model’s maximum output tokens.

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If the model stops before completing the requested answer, inspect both the model’s output-token maximum and the request’s output allowance. A valid request can still produce a partial result if the response runs out of room.

Classify API errors before retrying

Do not treat every failed request as transient. The troubleshooting guide recommends exponential backoff with jitter and a retry limit for temporary service or rate-limit failures such as 429 or 503. Its Python SDK guidance says transient errors are automatically retried up to four times, with an initial delay of about one second and a maximum delay of 60 seconds; behavior may vary by SDK version.

For client errors such as 400, 402, or 403, inspect the actual error cause instead of retrying blindly. A malformed request, access restriction, or other client-side issue usually needs correction rather than another identical request.

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Check safety blocks and generation termination

If a response is blocked or stops unexpectedly, inspect the safety settings and determine whether the prompt or generated completion triggered a filter. For an API response marked RECITATION, Google says the output may resemble certain data; its guide suggests making the prompt or context more unique and trying a higher temperature.

Parameter changes are not a universal repair. Google cautions that changing Gemini 3.x generation parameters from their defaults can cause unexpected looping or degraded performance in complex reasoning tasks. Diagnose the termination reason first and follow guidance for the model and symptom involved.

Ground answers in suitable evidence

Recent or obscure public facts

When an answer depends on current or obscure information, consider Google Search grounding. Google’s prompt strategies say Search grounding connects Gemini to real-time web content and should be enabled when the model may need recent or obscure facts. Inspect the grounding chunks and supports: the presence of grounding does not by itself prove that the answer’s claims are supported.

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For answers based on internal documents or websites, use an appropriate connected retrieval source, such as Agent Search, and check whether the retrieved material is relevant and complete. Google’s Agent Platform grounding documentation describes grounding with connected data. Review the returned evidence against the answer rather than assuming retrieval guarantees correctness.

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Grounding metadata may be absent when source relevance is low or the response contains incomplete information, according to Google’s grounding documentation. If useful evidence exists but the answer ignores it, check retrieval coverage, relevance, where the evidence appears in context, and whether the instructions clearly require answering from that material.

Questions that require calculation

For arithmetic, counting, or other computation, use code execution where appropriate instead of relying on free-form generation. For ambiguous or multi-step instructions, make the task and constraints explicit. With long context, Google’s prompting guidance recommends putting the context first and the specific instruction or question at the end.

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Trace tool use from selection through interpretation

A tool-related failure can happen at several distinct points. Check each one rather than treating every bad tool answer as a prompt problem:

  • Selection: Did the agent choose the intended defined tool, or omit a required call?
  • Arguments: Were all required arguments supplied with the correct types and meanings? Look for invalid syntax or missing fields.
  • Execution: Did the endpoint run successfully, with valid authentication and permissions, or did it time out or return an internal error?
  • Returned data: Was the result relevant and sufficient to answer the question?
  • Interpretation: Did the model use the returned data correctly?

Agent Platform’s evaluation documentation distinguishes these kinds of failures, including incorrect selection, missing or wrong parameters, insufficient tool output, and execution errors. A prompt rewrite will not fix an unavailable endpoint or bad permissions, and a tool outage differs from the model misreading valid output.

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With client-side function calling, the application—not the model—must execute the requested function and return its result along with the conversation parts so the model can continue. Check that handoff in Google’s function-calling documentation.

If the agent repeatedly calls a tool, check that the endpoint is available and that the conversation preserves the relevant prior context. Google’s troubleshooting guide identifies loss of previous context and forced calls to unavailable endpoints as possible causes; it suggests concise instructions such as “Provide the answer once.”

Diagnose incomplete answers, repetition, and formatting loops

Requested content is missing

Write down what a complete response must contain, then check the trace to see whether the agent stopped during reasoning, a tool call, or final response generation. Agent Platform identifies “Incomplete Execution” as a failure pattern for tasks that are only partly completed or stop prematurely. In an evaluation, CX Agent Studio can set expectations for a message, a tool call, or an agent handoff.

Output is repetitive or malformed

Google’s troubleshooting guide treats repeated tokens, repeated newlines or duplicate text in structured output, repeated tool calls, and repetitive unstructured text as distinct symptoms. Depending on the case, inspect whether schema field ordering conflicts with the output schema, whether the prompt includes escaped Unicode or tab sequences, whether a tool endpoint is unavailable, or whether the request cannot be resolved.

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Do not change temperature reflexively: Google’s recommendations are scenario-specific, and its guidance for complex Gemini 3.x reasoning tasks is to keep the default parameters.

Use evaluations and traces to verify a fix

  1. Create representative cases. Include prompts that reproduce the failure, the required answer content, and expected tool calls or results where relevant. CX Agent Studio supports expectations for a message, a tool call, or an agent handoff.
  2. Review evaluation results. Check summary metrics, inspect failed cases, and group related failures into clusters.
  3. Open traces. Follow inputs, calls, and results turn by turn to find where the failure begins.
  4. Make one targeted change. Classify the cause as an instruction, tool-configuration, or data-quality issue, and change the relevant layer.
  5. Rerun the evaluation. Compare results with the original cases to check whether the change addressed the recurring failure.

This is more informative than judging one answer in isolation: a single example can change by chance, while representative cases can show whether the underlying failure pattern changed.

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AI agentsAI troubleshootingfunction callingGeminiGoogle Gemini APIgrounding

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