Operational AI Analytics Dashboard

Context

Many AI workflows and agents ran in production, but there was no single view of their state and cost.

Problem

Degradation and overspend were noticed after the fact — from a complaint or an invoice — with no decision or cost tracing, so the cause could not be found.

Constraints

Low telemetry latency, correct attribution of cost to a workflow, decision auditing.

Architecture

Telemetry collection → aggregation → workflow state → live dashboard and threshold alerts.

AI layer

Anomaly detection in latency, cost and escalation share — to see a problem before it shows in the result.

Event model

Workflow steps emit telemetry events: cost, latency, the decision made; the dashboard builds on the stream, not periodic exports.

Integrations

Workflow runtimes, model billing and incident trackers connected through a normalized layer.

Automation flows

Threshold alerts, automatic incident creation tied to the specific workflow and step.

Infrastructure

Streaming aggregation, metric storage with retention, idempotent telemetry ingest.

Observability

This is the observability layer itself: agents, workflows, errors, costs and states — in one place and in real time.

Results

Degradation and overspend are visible immediately, response is faster, and the cause can be established from traces.

Lessons

Without observability an AI system degrades invisibly and undebuggably; a “system average” does not show which step eats the budget.