ADIAS is a framework for automated design of interactive agentic systems. Existing methods are largely candidate-centric, which leaves the repair progress implicit and leads to inefficient repair targeting, slow consolidation of partial progress, and propagation of ineffective interventions across rounds. To address this, we formulate issue-centric agent optimization, where repair progress is carried forward as an explicit persistent issue state to guide optimization. ADIAS includes two mechanisms: persistent issue state and issue-guided optimization. The persistent issue state maintains stable issue identities, lifecycle status, supporting evidence, and intervention-outcome histories. Issue-guided optimization uses this state to jointly propose repair targets and revision directions for subsequent focused full-code modification. Across five interactive benchmarks, ADIAS outperforms the strongest baseline by 25.2% on average and achieves consistent gains across four backbone models. Controlled ablations further show that removing persistent issue state or replacing issue-centric revision with candidate-centric policies leads to performance drops of up to 40.7%. Blogger's Review: ADIAS framework achieves automated design of interactive agentic systems through issue-centric optimization, significantly improving performance and efficiency. The advantage of this method lies in its ability to explicitly track repair progress and problem states, enabling more effective optimization and revision.