Abductive reasoning over knowledge graphs (KGs) seeks a first-order logic hypothesis whose answer set explains a given set of observed entities. Since many hypotheses can explain the same observations, controllable hypothesis generators condition generation on entities, relations, or logical patterns, but they treat generation as a single step. A generated hypothesis may be well-formed and satisfy the given conditions yet still fail to explain the observations, and the model has no mechanism to detect or correct this mismatch. Closing the abductive cycle requires revising a discrete, structured hypothesis, which large language models cannot do reliably, even though they iterate readily in natural language. We propose HypoAgent, an agentic hypothesis refinement framework that treats condition signals not only as expressions of user intent but also as operators that steer generation. A Hypothesis Proposal Agent calls a small trained generator to propose a hypothesis. Root-cause analysis then uses fragment-level coverage to locate branches worth inspecting and gathers neighborhood evidence from the training graph. A Hypothesis Refiner Agent combines this diagnosis with previously generated hypotheses to produce updated conditions and directly revised hypotheses. HypoAgent outperforms one-shot generation in single-turn and multi-turn settings on BioKG, PharmKG8k, and DBpedia50, and in the unconditional setting on DBpedia50. Our code is available at https://github.com/HKUST-KnowComp/HypoAgent.

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