Semantic observability and agent telemetry: maximize AI agent reliability

Semantic observability and agent telemetry are presented as complementary practices for governing enterprise AI agents by recording not only technical execution details but also an agent’s objectives, evidence, permissions, policy checks, evaluations, and business outcomes. While traditional application monitoring can confirm successful API calls, uptime, latency, and resource use, it cannot determine whether an agent selected appropriate tools, relied on authoritative sources, followed required rules, or produced an acceptable decision. The text argues that correlated traces, metrics, logs, and semantic context can expose issues such as reasoning loops, improper tool actions, retrieval of outdated information, missing evidence, hallucinations, and cost drift before they affect users. It recommends using OpenTelemetry generative-AI conventions to standardize data across frameworks, maintaining trace context through model calls, tools, queues, and sub-agents, and distinguishing technical success from task success and evaluation results. Effective implementation includes controlled data collection and masking, schema governance, outcome-based tail sampling, dashboards combining quality, cost, latency, and drift indicators, and clear ownership for investigations and fixes. Dataiku is described as a platform that integrates these observability, evaluation, governance, and business-performance capabilities for agents across multiple systems.

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