This work argues that autonomous scientific systems should emit portable, claim-aware artifact lineage as a minimum audit layer, and proposes a compact observability profile organized around individuals, operators, fitness records, lineage, archives, runs, streams, and steering commands.
Abstract
Autonomous scientific agents now increasingly propose ideas, write code, run experiments, analyze results, and even draft papers. Observe and audit those agents are necessary but logging every model call is not enough, scientists also need to inspect the artifacts and claims that the systems produced and their relations. This is driven by the fact that failures in scientific agent systems are often distributed across several objects. A manuscript claim may cite the wrong evidence, a search process may select a degenerate candidate, a laboratory novelty claim may depend on an unstated rule, or a multi-agent plan may change without a visible trigger. Existing tracing, experiment tracking, and archival provenance tools are valuable, but their native objects do not make these scientific audit relations first-class. We argue that autonomous scientific systems should emit portable, claim-aware artifact lineage as a minimum audit layer. We propose a compact observability profile organized around individuals, operators, fitness records, lineage, archives, runs, streams, and steering commands. In this profile, scientific claims are ordinary individuals with explicit evidence bindings and verification records. The profile is intended as a semantic layer that complements current telemetry and provenance standards. Execution details can remain in OpenTelemetry. Final packages can export to PROV-O or RO-Crate standards.
NetArtifactBench is introduced, which tests whether AI agents can repair inconsistent records derived from public network-system artifacts while preserving claims that remain supported, and argues that artifact integrity should become a first-class design and evaluation requirement for AI agents operating on network sy...
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A pipeline promoting an AI system publishes records claiming the thing evaluated is the thing deployed and that the evidence licensed the transition, and measures whether those records can express that claim and whether it holds where declared.
RADAR is an auditable runtime that makes an agent's analytical choices inspectable and supports their revision through execution feedback and rejects the operation or provides diagnostic feedback, allowing the agent to revise its choices before errors propagate.
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Tracekit is an open-source, dependency-free system that captures three channels for every agent session: what the human asked (intent), what the model said of its reasoning (self-report), and what it actually executed (actions).
Long-horizon agents increasingly operate across many steps, tools, and observa- tions. In this setting, the relevant oversight question is not only whether each action is locally valid, but whether the evolving trajectory still corresponds to the task the user authorized. Drift can accumulate quietly: an agent may call...
HANSARD is proposed, a reference architecture treating accountability as a life-cycle property, i.e., spreading an act across redundant agents until none is a but-for cause, making laundering visible.
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