Category: AI Observability & Monitoring

When one user request fans out across agents, tool servers, queues and callbacks, one missing trace header splits its evidence into traces that no backend can rejoin by trace ID. We built a two-agent, six-tool chain instrumented with the OpenTelemetry Python SDK 1.45.1 over real localhost transports (a thread-pool hand-off, agent-to-agent JSON-RPC over HTTP, Model Context Protocol (MCP) calls w...

Teams that must explain and trace production models can rent the capability or build it from open-source parts; we ask what each choice costs in latency, fidelity and money. We benchmarked the estimators that managed tabular services expose (Kernel SHAP, as in SageMaker Clarify, and sampled Shapley with a path count, as in Vertex Explainable AI) against exact TreeSHAP and gradient methods on XG...

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 ...

The rapid deployment of multi-modal AI systems—integrating vision, language, and sensor streams—has exposed 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 remediation...

Observability in federated learning 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, define hi...

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...

The legal services sector is undergoing a profound transformation driven by artificial intelligence technologies that reshape economics and workflows across core domains. This article systematically investigates AI’s impact on e-discovery, contract analysis, legal writing, and courtroom preparation, addressing three critical research questions: (RQ1) How has AI altered cost structures and effic...

As AI-driven predictive maintenance (PdM) systems become integral to smart manufacturing operations, ensuring the quality and reliability of their explanations is critical for safety, compliance, and operational trust [1]. This article extends the AI observability framework to manufacturing AI systems, focusing on explanation quality monitoring in predictive maintenance contexts. We define a sp...

Modern AI systems deployed in production remain fundamentally opaque to the engineers who operate them. While OpenTelemetry has emerged as the de facto standard for distributed systems observability, its extension to AI and large language model (LLM) workloads exposes critical gaps: latency traces do not capture hallucination rates, infrastructure metrics do not surface semantic drift, and no v...