The results are in: More of you are using premium Gemini than we expected

In my hands-on experience with Gemini, I've noticed instances where the system claims a task is finished, but I'm unable to confirm the desired result. This gap in verification needs addressing among developers. A certain completion message influences a user's next steps: trusting the outcome, repeating an operation, or investigating an error. If the message isn't backed by the tool's actual result, the user ends up performing the verification themselves.

This feedback is based on my personal experience and not a benchmark or claim about all Gemini models or integrations. I haven't provided a minimal reproducible example, specific model/version details, or request/response traces. Therefore, I can't pinpoint the cause to the model alone, but rather to the application, orchestration layer, or tool integration.

My proposed solution: the assistant should describe an action, indicate the tool call was made, state if the tool returned a success response, and verify the resulting state against the requested outcome. These states should not all be described as "done." If a tool fails, access is unavailable, or the final state can't be checked, the response should clearly state that.

For workflows that modify something, checking the resulting state before reporting completion is preferred when practical. When only part of a task succeeds, the response should specify that part and what remains unresolved.

I'm seeking insights from other developers on how they ensure completion claims in Gemini function-calling workflows are tied to actual tool results. What patterns have worked for ambiguous tool responses, partial success, and post-action verification? I'd also appreciate suggestions for the most useful details to include in a reproducible follow-up report. I previously shared this concern on the Google AI Developers Forum and am keen on practical methods to enhance workflow reliability and status message accuracy.