OpenTelemetry is an open standard and set of tools designed for collecting, processing, and exporting telemetry data from distributed applications and systems. As a vendor-neutral, community-driven project, OpenTelemetry enables organizations to instrument their software to generate standardized telemetry—including traces, metrics, and logs—without creating tight dependencies to specific observability backends or vendors ¹⁾.

OpenTelemetry addresses the complexity of observability in modern, distributed architectures by providing a unified framework for instrumentation. Rather than requiring developers to integrate multiple vendor-specific SDKs or proprietary agents, OpenTelemetry offers a single set of APIs and libraries that can emit telemetry data in standardized formats. This approach decouples the instrumentation layer from the backend observability platform, allowing organizations to switch between observability solutions or use multiple backends simultaneously without re-instrumenting applications ²⁾.

The three primary telemetry signals supported by OpenTelemetry are traces (which track requests through distributed systems), metrics (which capture numerical measurements over time), and logs (which record discrete events). This comprehensive approach enables holistic observability across complex, multi-service environments.

OpenTelemetry's architecture comprises several key layers. The instrumentation layer includes language-specific SDKs and automatic instrumentation libraries that capture telemetry from applications and libraries. These instrumentation components are available across multiple programming languages including Java, Python, Go, .NET, Node.js, and others.

The processing layer uses OpenTelemetry Collectors—standalone, vendor-agnostic applications that receive telemetry data from instrumented services, apply transformations, sampling, and filtering, and forward processed data to observability backends. Collectors support multiple protocols including OTLP (OpenTelemetry Protocol), Jaeger, Prometheus, and others, providing flexibility in data ingestion and export patterns ³⁾.

The export layer enables sending processed telemetry to various backends including open-source platforms like Jaeger and Prometheus, or commercial observability solutions from vendors such as Datadog, New Relic, Splunk, and others. This vendor-neutral approach prevents lock-in and allows organizations to optimize their observability stack based on specific requirements.

OpenTelemetry Ingestion refers to the automated collection and standardization of telemetry data—including metrics, traces, and logs—from coding tools, AI agents, and development environments into centralized data platforms. This approach leverages the OpenTelemetry framework to enable organizations to monitor, analyze, and govern the behavior and performance of AI-assisted development tools at scale ⁴⁾.

The ingestion process captures structured telemetry from multiple sources—including language model API calls, code generation workflows, agentic reasoning traces, and tool execution patterns—and centralizes this data into managed data lakehouse environments ⁵⁾.

The ingestion architecture typically consists of three components: instrumentation at the source layer where telemetry is emitted, collection through standardized exporters and receivers, and storage in structured table formats that enable downstream analysis. In modern AI governance contexts, ingested telemetry is often written to Delta Lake tables managed through data catalogs like Unity Catalog, providing both technical observability and business governance capabilities ⁶⁾.

The ingestion process begins with instrumentation of coding tools and AI agents using OpenTelemetry SDKs available for languages including Python, Java, Go, and JavaScript. These SDKs automatically capture spans (representing individual operations with timing and status information), metrics (quantitative measurements like latency percentiles, token usage, and error rates), and logs (structured event records).

The collected telemetry flows through OpenTelemetry exporters that transform data into standardized formats (such as OTLP—OpenTelemetry Protocol) and transmit it to backend systems. For AI agent governance scenarios, ingestion endpoints receive this telemetry and parse it into Delta tables, which support ACID transactions, schema evolution, and efficient columnar queries. This approach enables organizations to apply data governance policies including access controls, lineage tracking, and retention policies while simultaneously enabling real-time streaming analytics ⁷⁾.

Key technical considerations for ingestion include:

* Sampling strategies to reduce storage volume while maintaining statistical representativeness

• Telegraf
• OpenRouter
• Prometheus