# Before Agents Act: Assurance-Aware Semantic Scheduling for Evidence Acquisition in Distributed Systems

arXiv: https://arxiv.org/abs/2609.34376 · HTML: https://mlsanity.com/paper/2609.34376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://mlsanity.com/paper/2305.05176/) (2023)
- [RouteLLM: Learning to Route LLMs with Preference Data](https://mlsanity.com/paper/2406.18665/) (2024)
- [Cognitive Admission Control: Risk-Conditioned Assurance for Consequential Actions in Agentic Distributed Systems](https://mlsanity.com/paper/2609.16313/) (2026)
- [The Illusion of Independent Quorums: Epistemic Fault Domains and Correlated Cognitive Failures in Agentic Quorums](https://mlsanity.com/paper/2609.02925/) (2026)
- [ToolGuardian: Declarative Security for AI Agent-Tool Interactions](https://mlsanity.com/paper/2607.21835/) (2026)
- [Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures](https://mlsanity.com/paper/2609.10969/) (2026)

## Built on by (most cited first, 1 of 1)

- [KedgeFlow: A Programmable Control Plane for Enterprise AI Agents](https://mlsanity.com/paper/2610.14810/) (2026)

## Abstract (arXiv)

Tool-using agents can initiate consequential infrastructure changes, yet evidence required for admission may expire while other checks run or depend on a shared fault domain. We formulate evidence acquisition as joint witness selection and scheduling under quorum, diversity, freshness, deadline, and resource constraints. Assurance-Aware Semantic Scheduling (AAS) combines integer-program selection, dispatch-aware temporal scheduling, bounded diagnostic expansion, and receipt-aware repair. Formal results state the assumptions needed for dispatch-time freshness and finite diagnostic expansion. In three generated infrastructure workloads, AAS produces 1,075/1,200 valid candidates versus 647/1,200 for constraint-aware forward scheduling; stale candidates fall from 440 to 12. Paired sensitivity studies reuse the same instances and operation latency draws across parameter settings. A corrected timeout intervention finds 18/20 admissions with repair or full resynthesis versus 0/20 for a static plan, with lower committed cost when receipts are reused. On 20 constructed cases requiring a certified decomposition cut, refinement recovers an oracle-matching feasible plan every time. These are controlled simulation results; the bounded oracle shares a temporal search component, and transfer to deployed systems remains untested.