Category: AI Observability & Monitoring

Generating counterfactual trajectories that illustrate how observed anomalies would shift under alternative feature conditions, providing intuitive explanations for model behavior.

Training reinforcement agents to dynamically reconfigure monitoring resources (e.g., sampling rates, logging granularity) in response to observed latency spikes and anomaly patterns.

Scientific discovery increasingly relies on the ability to integrate heterogeneous data sources, a challenge exemplified by drug discovery and materials design where molecular, imaging, and experimental data must be synthesized to generate testable hypotheses. This article addresses the growing need for robust multimodal artificial intelligence frameworks that can accurately predict scientific ...

Measuring and mitigating the environmental impact of large-scale AI deployments has become a critical concern for both industry and academia. While prior work has focused on static carbon accounting for training pipelines, far less attention has been paid to the real-time carbon intensity of inference workloads in production. This article investigates how low-latency carbon emission estimators ...

Observability in decentralized edge AI systems remains a critical challenge due to the tension between the need for detailed system insights and the imperative to protect sensitive user data. Traditional observability approaches rely on raw telemetry that often contains personally identifiable information (PII) or proprietary model parameters, making them unsuitable for privacy‑preserving deplo...

The rapid deployment of multi-modal AI systems—integrating vision, language, and sensor streams—has e[REDACTED]sed critical gaps in interpretable fault diagnosis and root-cause attribution. This article proposes a causal graph–based observability framework that embeds probabilistic causality into the data flow of AI pipelines, enabling precise identification of failure origins and adaptive reme...

Observability in federated l[REDACTED]g environments suffers from fragmented taxonomy of artifacts, hindering coherent reporting and cross‑agent integration. This article introduces a standardized taxonomy that classifies observability artifacts across distributed agent ecosystems, enabling interoperable metadata exchange and unified analytics. We formalize eight core artifact categories, defin...

The rapid proliferation of autonomous AI agents operating across decentralized infrastructures has intensified the need for coherent observability frameworks that can consistently capture, categorize, and report on system states. Existing approaches vary widely in scope, granularity, and semantic alignment, leading to fragmented reporting practices that hinder cross-agent collaboration and long...

Academic Citation: Ivchenko, Oleh, Ivchenko, Iryna (2026). Human‑in‑the‑Loop Auditing: Structured Interaction Patterns for Trust Calibration in High‑Stakes AI. Research article: Human‑in‑the‑Loop Auditing: Structured Interaction Patterns for Trust Calibration in High‑Stakes AI. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.21753075  · ...

Predictive observability addresses the growing need for anticipatory monitoring of complex system behaviors. This article introduces synthetic counterfactual data generators that simulate future distribution shifts, enabling organizations to preemptively recalibrate monitoring pipelines. We propose a novel framework that leverages probabilistic forecasting and synthetic data synthesis to infer ...