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OneAgent for GenAI

OneAgent for GenAI

Monitor and trace your AI workloads and apps automatically with OneAgent.

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Smartscape support for AI Agents topology and dependency map showing multi-agent call with per-agent LLM req counts, token usage, and resp. timeTrace-level debug view showing agentic span hierarchy, tool calls, prompt and response content, and agent topology for a single AI agent run.Prompts stream showing all Agent and LLM interactions with model version, token count, and full prompt/response content.Evaluation performance overview for gen_ai applications
  • Product information

Overview

Dynatrace OneAgent automatically discovers and instruments AI workloads through Python code module sensors. Install OneAgent once and get immediate visibility into Agent or LLM calls, token usage, latency, cost, and agentic workflows — with no changes to your application code, no OTel SDK to configure, and no instrumentation library to maintain.

OneAgent sensors follow the OpenTelemetry GenAI semantic conventions, emitting gen_ai.* spans and metrics that populate the AI Observability app out of the box. Stable sensors are available for OpenAI, AWS Bedrock, and LangChain. Experimental sensors extend coverage to Anthropic, Cohere, CrewAI, Groq, Haystack, Mistral AI, Ollama, and Google GenAI.

For AWS Bedrock, OneAgent goes beyond basic LLM tracing to provide dedicated guardrail observability — tracking PII detection, topic filters, word filters, and content filters as first-class metrics with latency and activation counts per guardrail.

Use cases

Zero-config AI monitoring

  • Capture LLM calls, token usage, and response latency automatically as soon as OneAgent is deployed — no instrumentation code, no restart required.
  • Get gen_ai.client.token.usage and gen_ai.client.operation.duration metrics and traces immediately for OpenAI, AWS Bedrock, and LangChain workloads.
  • Enable prompt and response content capture via a single OneAgent setting (opt-in, disabled by default to protect sensitive data).

End-to-end agentic and pipeline tracing

  • Trace full LangChain pipeline executions — RAG chains, agentic loops, tool calls, and retrieval steps — as structured span hierarchies with dt.gen_ai.operation.kind labels (workflow, task, agent, tool, retrieval).
  • Correlate agent decisions, tool invocations, and LLM responses in a single distributed trace with W3C Trace Context propagation across all monitored services.
  • Debug failures at the exact step where a chain breaks, with exception attributes recorded on the failing span.

Guardrails observability for AWS Bedrock

  • Monitor guardrail activations, latency, and filter hits as dedicated metrics broken down by guardrail ID, filter type (PII, topics, content, words), and input/output direction.
  • Track prompt caching efficiency — cache read and write token counts — to understand cost savings and cache effectiveness per model.
  • Detect guardrail-related regressions in production without adding any application-side instrumentation.

Broad AI ecosystem coverage

  • Stable sensors for OpenAI (Chat Completions, Completions, Embeddings), AWS Bedrock (Invoke, Converse, streaming), and LangChain.
  • Experimental sensors for Anthropic, Cohere, CrewAI, Groq, Haystack, Mistral AI, Ollama, and Google GenAI — covering the full Python AI ecosystem on an accelerated delivery schedule.
  • All sensors emit consistent gen_ai.* attributes and metrics regardless of provider, enabling unified dashboards across a multi-provider AI stack.

Get started

OneAgent AI sensors require no application changes. Deploy or update Dynatrace OneAgent with the Python code module enabled, and AI workloads are automatically discovered and instrumented. To enable prompt and response content capture, turn on the opt-in setting in OneAgent configuration.

To get started, see the Dynatrace OneAgent AI Observability documentation and the AI Observability app overview.

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