Observability

Every run traced, scored, and costed

You cannot operate an AI workforce you cannot measure. AgentLabz instruments the whole agent loop, from the first token to the final tool call.

Latency

1.24s

-8%

p50 end-to-end response

Token Usage

18.4M

+4%

Rolling 30 days

Tool Calls

62,180

+11%

Successful invocations

Success Rate

96.2%

+1.4

Runs completed without error

Escalation Rate

12.7%

-2.1

Handed to a human

Cost

$0.031

-14%

Per resolved conversation

Cache Hit Rate

74%

+6

Prompt and KV cache reuse

Grounding Flags

0.7%

-0.3

Responses flagged by grounding checks

Avg Resolution Time

3m 12s

-19%

Open to resolved

Topic Clusters

142

+9

Auto-clustered conversation intents

app.agentlabz.tech / analytics

Resolution rate

96.2%

Cost / resolution

$0.031

p50 latency

1.24s

Token usage by agent · 30 days

app.agentlabz.tech / conversations

#48219 · Support Agent

Duration 2m 40s

Resolved

#48218 · Voice Agent

Duration 5m 02s

Escalated

#48217 · Sales Agent

Duration 1m 18s

Meeting booked

#48216 · Finance Agent

Duration 3m 55s

Resolved
Capabilities

Built for teams that own an SLO

Run traces

Every step, tool call, retrieval, and model invocation is captured as a span you can replay.

Evaluation sets

Score new agent versions against golden examples and live traffic samples before promotion.

Grounding checks

Answers are verified against retrieved sources; unsupported claims are flagged and counted.

Cost attribution

Spend is attributed per agent, per workflow, and per resolved conversation.

SLO alerting

Burn-rate alerts on latency, error, and escalation objectives, routed to Slack or PagerDuty.

OpenTelemetry export

Stream spans and metrics into your existing observability stack.