Arya.ai studies recovery in AI agent workflows
The authors present a study on durable AI agents that can resume actions when authority changes and outcomes remain uncertain. The research isolates recovery decisions from language‑model behavior, examining checkpoint state, permission to dispatch, evidence of external effect, and permission to reconcile.
Key points
- 336 histories tested on LangGraph and Temporal hosts
- 42 histories passed retained‑ledger reconciliation
- InfinityOS component study includes 16 cases
The study ran 336 histories on native LangGraph and Temporal hosts, covering 14 schedules, three provider contracts and four application policies. All 168 paired host cells produced identical outcomes. Retained‑ledger reconciliation passed all 42 histories: 21 completions, 15 denials and six unknown outcomes. Cached retry, revalidation before retry and stable‑key retry passed 30, 36 and 31 histories respectively.
A separate 16‑case component study of InfinityOS, an enterprise AI platform by Arya.ai, exercised exported authority, worker, recovery and settlement code over repository fakes, isolating the roles of current authority, provider contracts and retained observations in recovery, with safety, completion and unresolved obligations reported separately for the declared application profile.
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