AI Agent Observability

Run high-volume LLM workloads on production-proven tracing infrastructure

Unify tracing, testing, experiments, and evaluations in one platform across dev and prod

Monitor cost, latency, and output quality with actionable alerts and built-in access controls

Trace failures across frontend sessions, LLM execution, and backend services in a single view

Continuously Evaluate and Enhance the Quality of Your AI Responses

Easily spot and address quality concerns, such as missing responses or off-topic content, with out-of-the-box quality evaluations
Detect hallucinations and improve business-critical KPIs with custom evaluations aligned to your KPIs
Build and version golden datasets from real production traces, and use human review and annotation to label outputs at scale
Detect drift by isolating low-quality prompt-response clusters with similar semantics

Automatically catch inaccurate responses before they reach users by flagging contradictions and unsupported claims with Datadog's hallucination detection
Customize detection sensitivity for your use case—flag only critical contradictions, or both contradictions and unsupported claims
Pinpoint root causes by drilling into full traces to see the exact hallucinated claim and where it failed in the chain
Improve models over time by tracking hallucination trends by model, tool call, or environment

Get full visibility into every experiment run with automatic tracing that captures evaluation scores, latency, errors, and token usage
Compare prompts, models, and configurations side by side against the same production-derived datasets to see which performs best before rollout
Compare quality, cost, and latency across releases to catch trade-offs before they reach production
Keep testing repeatable across teams with versioned datasets and shared performance analysis in one place

Investigate the root cause of hallucinations, low-quality outputs, and other anomalies with complete trace visibility across your LLM chain
Debug complex RAG workflows by pinpointing and correcting errors in embeddings, retrieval, and context injection steps
Connect LLM traces to backend microservice, API, and infrastructure metrics for full-stack root cause

Defend against direct and indirect prompt injection attacks by scanning prompts, responses, and retrieved content for malicious patterns before they can be executed
Monitor MCP server interactions to detect unauthorized tool changes, credential exposure, and unusual activity patterns (tool poisoning, rug pulls, consent-fatigue exploitation)
Secure your RAG pipelines by detecting and tracing malicious instructions seeded in vector databases and identifying the exact documents used in each model response