Sessions

A trace is one interaction (a root span plus its child steps); a session is the conversation those interactions belong to - every trace sharing a session_id. Sessions catch the failure mode single-trace monitoring can’t see: every individual reply looks fine, but the conversation as a whole goes in circles, contradicts itself, or never resolves. Grouping is opt-in per call - pass any stable per-conversation id:

with client.tracer.trace("support-agent", session_id=conversation_id) as span:
    ...

Traces sent without one get their own auto-generated session.

The Sessions view

Observe → Sessions lists each conversation with:

Column	Meaning
Turns / spans	Root interactions and total recorded steps
Errors	Failed spans anywhere in the conversation
Judge Score	The lowest verdict among enabled session evaluators that scored it, with the judge that gave it named

Opening a session shows every turn in order; each turn’s full span tree is one click away.

PLACEHOLDER: screenshot of the session detail dialog - a list of user/agent turns in order with per-turn latency, a Judge scores section showing two evaluator verdicts with ratings, and an 'Add as test case' button in the header. — A session's detail view: turns in order, judge verdicts, and Add as test case.

Conversation-level judging

Two kinds of judge score a session as a whole, both triggered automatically when the conversation goes quiet:

Session Baseline Judge - built-in: goal progression, consistency, non-repetition. Also runs on demand from the session’s detail view. Its rubric lives in an evaluator config (tunable via Tune judge); pause it from the Scorers page’s row toggle.
Session-scoped online evaluators - your own criteria with scope="session", judged on idle and re-judged if the conversation resumes. See Multi-Turn Session Evaluation.

A verdict below the evaluator’s alert threshold raises a signal in the normal triage queue. Both are also callable from the SDK - run the coherence check on demand and read the session’s spans (e.g. to walk a reported drift span up to the turn it belongs to):

score = client.monitor.sessions.coherence_check(session_id)   # one judge call
print(score["rating"], score["justification"], score["driftSpanId"])

spans = client.monitor.sessions.spans(session_id)             # roots + children, oldest first

Where sessions come from

Source	How
SDK	`session_id=` on `tracer.trace(...)`
Framework integrations	Pass `session_id` to the handler/processor constructor
OpenTelemetry	`session.id`, `gen_ai.conversation.id`, or `agentx.session_id` span attributes
Importers	`agentx-moveworks` (one session per conversation) and `agentx-databricks` (`mlflow.trace.session` metadata)
Playground	Conversation simulation records each run as a real `sim-<id>` session

From finding to regression test

A failed conversation is a future regression test: Add as test case in the session detail turns the whole conversation into a multi-turn golden dataset case - see Datasets from Production.

Tracing

Framework Integrations

The Sessions view

Conversation-level judging

Where sessions come from

From finding to regression test

​The Sessions view

​Conversation-level judging

​Where sessions come from

​From finding to regression test

The Sessions view

Conversation-level judging

Where sessions come from

From finding to regression test