Agentic approaches for automated test generation can reach high-quality results, but they improve a given test suite incrementally, so a weak starting point may lead to many iterations or insufficient tests. To address this, we present Testuary, which provides the agentic phases with an improved starting point. Testuary first generates multiple test suites through independent, parallel zero-shot requests, checks and repairs them deterministically, and combines them into a single suite with a novel structural merging approach. A runtime-weighted set-cover reduction removes redundant tests without losing covered lines, branches, or killed mutants. After that, two agentic phases target the remaining gaps in coverage and mutation score. We compare Testuary with established search and LLM-based test generation approaches. The non-agentic first phase alone already significantly outperforms the current state-of-the-art. After the agentic phases, Testuary achieves on average more than 99.9% line and branch coverage as well as a mutation score of 98.6%, significantly outperforming all compared baseline approaches. Testuary needs fewer model requests and its test suites are on average smaller and faster than those of the compared agentic approach.

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