confident-otel

Confident AI OpenTelemetry Export

Use this skill to instrument an AI application—an LLM app, agent, RAG pipeline, or chatbot—with raw OpenTelemetry so its traces land in Confident AI. No confident-trace package is needed. The job is exactly two things: export to the correct Confident AI OTLP endpoint and set the confident.* attributes Confident AI reads from each span.

Scope: AI Applications Only

Instrument only agent loops and planning, LLM calls, retrieval/vector search, and tool calls. Do not apply confident.* attributes to web servers, CRUD backends, database layers, infrastructure, or non-AI spans. If the target has no LLM, agent, retrieval, or tool-calling component, this skill does not apply.

When to Use vs the Other Skills

• This skill (confident-otel)—vendor-neutral OTLP export and raw confident.* attributes without the confident-trace package.
• confident-tracing skill—automatic integrations and custom SDK spans in Python or TypeScript.
• deepeval skill—evaluation suites, datasets, metrics, and test runs.

Prerequisites

• A project-scoped CONFIDENT_API_KEY.
• An OpenTelemetry SDK for the application's language.
• For Python, opentelemetry-sdk and opentelemetry-exporter-otlp-proto-http.
• Direct Confident AI Cloud export uses OTLP/HTTP, never gRPC.

How It Works

Point an OTLP/HTTP traces exporter at Confident AI with the x-confident-api-key header. Confident AI reads confident.* attributes from the spans. Parent/child nesting, trace IDs, sampling, status, links, and context propagation remain native OpenTelemetry concerns.

Workflow

1. Confirm the target is an AI application, then inspect for an existing TracerProvider, exporter, Collector, or APM pipeline.
2. Choose the direct Cloud endpoint from the API key's region prefix. Read references/endpoint-and-exporter.md.
3. Wire or repoint an OTLP/HTTP exporter with the x-confident-api-key header. For Python, start from templates/confident_otel_setup.py.
4. If the process emits unrelated OpenTelemetry spans, isolate Confident AI export so only AI spans reach it.
5. Set confident.span.* fields on spans and confident.trace.* fields for the whole trace. Read references/span-attributes.md and references/trace-attributes.md.
6. JSON-encode objects and metadata; use native homogeneous primitive arrays for string lists.
7. If the app already emits gen_ai.* semantic-convention attributes, read references/gen-ai-fallbacks.md before adding redundant fields.
8. Flush after active work completes and verify the trace hierarchy in Confident AI.

Core Principles

1. Instrument and export AI components only.
2. Prefer repointing an existing exporter over adding a duplicate pipeline.
3. The confident.* keys are language-neutral.
4. Direct Confident AI Cloud export always uses OTLP/HTTP. A Collector may use another protocol on its application-facing side.
5. Set confident.span.type explicitly when known; use gen_ai.* inference only as a fallback.
6. Never hardcode API keys or record secrets and unapproved sensitive content.
7. Preserve native OpenTelemetry trace IDs, parentage, sampling, status, resources, links, and propagation.

References

Topic	File
Endpoints, region selection, authentication, exporter wiring, and filtering	references/endpoint-and-exporter.md
Trace-level confident.trace.* attributes	references/trace-attributes.md
Span-level confident.span.* attributes and data-type rules	references/span-attributes.md
Standard OpenTelemetry gen_ai.* fallback behavior	references/gen-ai-fallbacks.md

Templates

Purpose	Template
Minimal Python OTLP exporter setup and example trace	templates/confident_otel_setup.py