A hands-on run produced a split scorecard — wins on some tasks, stumbles on others — and a verdict resting on Google's track record of second acts.
A split scorecard rarely makes a clean story, and ZDNet's hands-on run with Google's Gemini Spark delivers exactly that: tasks the agent handled capably, sitting alongside tasks where it plainly came apart.
The review frames the stakes as a binary — another Google AI fail, or the company's next dark horse — and its own testing declines to settle the question. The agent's performance was genuinely mixed. Where it worked, it worked well. Where it stumbled, the failures were visible enough to shape the reviewer's overall impression. That inconsistency is the finding that matters most for anyone tracking agentic systems: a tool that is only intermittently reliable demands a different evaluation calculus than one that is uniformly mediocre, because buyers and developers can engineer around a known weakness but not around unpredictability.
What ultimately tips the judgment is not the test itself but the company behind it. ZDNet invokes Google's track record as the reason to look beyond the missteps — an argument with real weight at a moment when agents are the industry's most contested build-out. The reviewer reads Google's history as evidence that early stumbles are not final verdicts, which is precisely the logic the dark-horse framing depends on. Strip that history away, and the raw results alone would read far more soberly.
The published verdict is therefore deliberately unresolved: neither write-off nor breakout, but a demonstration with caveats from a writer who chose to weight trajectory over the stumbles.
For the wider agent race, the episode is a reminder of how thin the public evidence base still is — single-reviewer hands-on tests, mixed outcomes, and conclusions resting as much on brand history as on measured performance. Whether Gemini Spark ends up filed under fail or dark horse will be settled by what Google ships next, not by what this one test showed.
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