Business Process Monitoring for AI-Driven Operations
Operations leaders, support leaders, product managers, transformation teams
Business processes rarely fail in one system. They slow down across APIs, queues, models, tools, handoffs, policies, and human review steps. By the time the metric moves, the root cause is already buried.
Process health from event to outcome
AnoSys ingests business process events, AI traces, application telemetry, evals, cost, and outcome data into one operational timeline. Teams can detect anomalies, evaluate process health, explain likely root cause, and route fixes with the process context intact.
Instrument the process path, monitor the operating signals, and identify where the client-facing issue started.
Ingest
Capture process events from APIs, apps, agents, queues, files, support tools, and custom systems.
Monitor
Track process health, cycle time, completion, quality, cost, SLA, and outcome signals.
Analyze
Find bottlenecks, failed handoffs, policy exceptions, and process regressions.
Resolve
Trigger alerts, workflows, reports, and remediation steps with root-cause context.
What supports it
Why teams buy it
- Reduce time to resolution by finding where workflows slowed, failed, or violated expectations.
- Connect AI behavior to business outcomes instead of monitoring systems in isolation.
- Give operations, product, and support leaders a live view of process health.
Is this process mining?
AnoSys is not limited to offline process mining. It ingests live operational signals, AI traces, evals, cost, and business events so teams can detect and act in production.
Can AnoSys monitor human and AI steps together?
Yes. Process units can represent agents, tools, APIs, queues, review steps, and human handoffs in one operational timeline.