ADIAS: Automated Design of Interactive Agentic Systems
Quick summary
arXiv:2608.06410v1 Announce Type: new Abstract: Automated agent design improves agent harnesses through iterative revision, evaluation, and feedback summarization. Existing methods are largely candidate-centric: cross-round experience is organized around candidate agents, which leaves the repair progress implicit. This causes inefficient repair targeting, slow consolidation of partial progress, and propagation of ineffective interventions across rounds. Therefore, we formulate issue-centric agent optimization, in which repair progress is carried forward as an explicit persistent issue state to
Key takeaways
- arXiv:2608.06410v1 Announce Type: new Abstract: Automated agent design improves agent harnesses through iterative revision, evaluation, and feedback summarization.
- Existing methods are largely candidate-centric: cross-round experience is organized around candidate agents, which leaves the repair progress implicit.
- This causes inefficient repair targeting, slow consolidation of partial progress, and propagation of ineffective interventions across rounds.
Why it matters
“ADIAS: Automated Design of Interactive Agentic Systems” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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