arXiv AI
Pollard: Governed Semantic Execution Trees for Framework-Independent AI Agent Control and Replay
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arXiv AI
Oct 27
Responsibility Boundaries for Agentic Systems: A Comparative Evaluation of Probabilistic Agentic Programming
The paper introduces Probabilistic Agentic Programming (PAP), a framework that explicitly defines responsibility boundaries for language‑model agents by linking mandate, authority, effects, and evidence. It compares PAP to various programming paradigms and agent systems across seven dimensions, and presents a mediation kernel that enforces checks before tool execution, operation‑bound approval, and resource limits. An exploratory study with seven business cases and multiple models demonstrates that PAP’s native Touba implementation achieves the highest or tied observation counts, supporting its effectiveness in managing agentic behavior.
arXiv AI
Sep 25
Stale Does Not Mean Unsafe: Guard Precision for Tool-Using LLM Agents under Infrastructure State Races
The paper investigates how tool‑using language‑model agents can safely commit changes to infrastructure when external state may change between read and commit. By distinguishing invalidating races from predicate‑preserving and irrelevant ones, the authors evaluate three commit‑time guard granularities—global epoch, read‑set version, and semantic commit predicate—using a deterministic simulator and three quantized model families. The study finds that only the complete predicate guard consistently eliminates unsafe commits, while freshness‑based guards block a large proportion of benign races and model‑side signals fail to replace precise semantic enforcement.
arXiv AI
Aug 25
STAGE: Stateful Translation to Agentic Graph Execution with Policy-Scoped Context and Deterministic Control
arXiv:2608.22538v1 Announce Type: new Abstract: Policy-governed agents must interpret case evidence while following an authorized procedure. We present \textsc{Stage}, an executable-graph framework t...
arXiv AI
Sep 1
Safe to Resume? Breaking Execution Continuity of Agent Execution via Rollback
arXiv:2608.29381v1 Announce Type: cross Abstract: AI agents are moving toward persistent, stateful execution across various applications, accumulating execution state and external effects that are co...
arXiv AI
Jun 4
Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems
arXiv:2606. 04104v1 Announce Type: cross Abstract: Agent systems execute through runtimes with very different control points: local coding tools, framework SDKs, managed agent platforms, API gateways, and observer-only integrations.
arXiv AI
Oct 26
Permission to Retry: A Study of Authorization and Recovery in AI Agent Workflows
The paper investigates how AI agents can reliably recover from changes in authorization while executing tasks that involve external tools. It defines distinct obligations—checkpointing, permission to dispatch, evidence of external effects, and permission to reconcile—and evaluates them through a finite conformance study on 336 histories across multiple hosts, schedules, and provider contracts. The study demonstrates that all paired host cells produce identical outcomes and that various retry strategies (cached, revalidation, stable-key) successfully pass the majority of histories, while a component study of InfinityOS further isolates the impact of authority, contracts, and observations on recovery.