AI Observability
Trace Agent Behavior
From Prompt to Outcome
Every LLM call, tool call, and handoff becomes an OpenTelemetry trace, ready alongside Datadog or Splunk, with four lines of Python or none.
Agent Failures Hide in Healthy Metrics.
Traces Reveal Them.
An agent can give the wrong answer without triggering an alert. A tool call times out, but the model responds as though it succeeded. The failure goes unnoticed until a user relies on the answer and finds it wrong. To investigate, the team needs more than the final response: it needs the sequence of steps that led there.
Lumenova AI brings agent runs and their spans into a searchable trace view. Teams can see the inputs, outputs, and timing behind a response, making failures easier to find, explain, and fix before more users encounter them.
Engineers and risk reviewers can work from the same record instead of piecing together separate accounts of the incident.
Capabilities
Trace Every Step. Control Every Record.
See every agent step, protect sensitive data before it leaves your systems, and keep a queryable record in your own storage.
Agent-Native Tracing
Capture LLM calls, tool calls, and retrieval steps in one trace, grouped into sessions. MCP servers and clients are traced across process boundaries.
Lightweight Instrumentation
Add the SDK with four lines of Python, or route your OpenTelemetry Collector to it. LangChain and LangGraph need a callback handler.
Pre-Send Masking
Mask sensitive fields in your own process before traces are sent, including spans from third-party instrumentation.
Customer-Owned Archive
Keep trace history as open Parquet files in your own object storage, queryable through the same API and readable with any tool.
Span-Level Cost
Capture token cost on each span using provider pricing and your own model aliases, tagged by the dimensions you allocate spend on.
Team Notifications
Send alerts to email, Slack, Microsoft Teams, webhooks, or ServiceNow ITSM, with incident status syncing both ways.
Prompt Versioning
Version prompts like code, deploy by label, compare, and roll back. Test changes in a playground that’s traced and scored like production.
Editor Trace Queries
Query traces, evaluations, and experiments from Claude Code, Cursor, or Copilot through a read-only MCP server with each user’s permissions.
How Traces Arrive
The SDK
Four lines of Python, with no changes to existing call sites.
Your OpenTelemetry Collector
Point it at the platform, with nothing added to the application.
Traces become agent sessions
LLM calls, tool calls, retrieval steps, and MCP land in one trace, grouped into sessions.
Notifications
Email, Slack, Microsoft Teams, webhooks, or ServiceNow ITSM.
Your current tools
The SDK can send the same traces to your own collector, and on to Datadog or Splunk.
Archive in your storage
Once switched on at deployment, history is kept as open Parquet files you can read without us.
Frequently Asked Questions
APM tells you whether services are healthy. Agent failures often don’t show up there: a tool call times out, the model answers as if it succeeded, and the error rate never moves. Agent-aware traces show each step behind a response, so you can see where the answer went wrong, not just that the service stayed up.
Both. Engineers use traces to debug. Risk and compliance teams use the same traces as evidence, because evaluation scores, policy decisions, and guardrail results link back to the trace they came from.
Yes, with the archive enabled. Trace history lives in your own object storage, so your existing retention, deletion, and legal-hold policies apply to it directly.
Yes. Access follows your roles, and custom roles can be exported as JSON and reviewed through GitOps like code. The read-only MCP server for code assistants also respects each user’s own permissions, so no one sees more through their editor than they could in the platform.
Evaluations run on live and historical traces, so a failure you find in production can be scored with the same evaluators you use before release.