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

Trace Every Step of Your Agents—From Prompt to Backend Service

Visualize every decision and action in your multi-agent workflows, from planning steps to tool usage, to understand exactly how outcomes are produced
Pinpoint issues fast by tracing interactions between agents, tools, and models to find the root cause of errors, latency spikes, or poor responses
Connect LLM traces to backend microservice, API, and infrastructure metrics to resolve issues in full-stack context—see when a slow database or starved GPU is the real problem
Link agent behavior to real user sessions in RUM and cut MTTR by tracing failures across frontend, agent execution, and backend in one platform

Keep costs under control by tracking key operational metrics like tokens, usage patterns, and latency trends across all major LLMs in one place
Take action as issues arise with real-time alerts on anomalies such as latency spikes, error surges, or unexpected usage changes
Surface opportunities for performance and cost optimization by drilling into detailed end-to-end data on token usage and latency across the entire LLM chain

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, and fix issues at the source in embeddings, retrieval, or prompt construction

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
Resolve regressions faster by isolating low-scoring test cases and inspecting tool calls, retrieval steps, and intermediate outputs in the execution trace
Keep testing repeatable across teams with versioned datasets and shared performance analysis in one place

Quickly investigate the root cause of hallucinations, low-quality outputs, and other anomalies with complete trace visibility across your LLM chain
Fix issues at the source, whether in embeddings, retrieval settings, or prompt construction, to improve reliability before you scale
Debug complex RAG workflows by pinpointing and correcting errors in embeddings, retrieval, and context injection steps
Feed resolved issues into performance monitoring to ensure improvements are reflected in cost, latency, and accuracy metrics over time