Towards Reliable General-Purpose Speech Editing Agents: Formulation, Design, and Empirical Analysis

General-purpose speech editing should satisfy requested changes while preserving content and acoustic properties that should remain unchanged. Specialized speech models provide complementary capabilities, but reliably composing them requires accurate observation, robust interpretation of complex editing intent, executable tool composition, and assessment of realized outcomes. We formulate agentic speech editing as a partially observable Markov decision process (POMDP), separating latent editing states, observations, belief, actions, environment transitions, reward, and policy revision. Guided by this formulation, we introduce X·Edit, a training-free speech editing agent that combines active evidence acquisition, optional belief materialization, structured tool planning, outcome evaluation, and feedback-guided contextual policy revision. On all 426 MMAE speech tasks, X·Edit achieves 54.30% instruction following, 93.05% consistency, and 20.89% exact match with up to three policy revision, performing as the state-of-the-art current speech editing system. Requiring both complete delivery and passing all native checks yields 20.19% delivered success. Controlled studies further show that acquired conditioning evidence improves fine-grained observation fidelity, explicit belief helps retain complex interacting requirements, and higher-level action representations reduce the burden of composing fragmented speech tools. Feedback-guided recovery substantially improves delivery, while gains in complete fulfillment remain smaller, highlighting reliable preservation, reward estimation, and candidate selection as remaining challenges. Together, our formulation, system design, and empirical analysis provide a principled framework and practical guidance for building reliable general-purpose speech editing agents.