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Maestro user guide
- Introduction
- Getting started
- Building with Maestro BPMN
- Understanding Maestro BPMN modeling
- Opening the modeling canvas
- Modeling your process
- Aligning and connecting BPMN elements
- Autopilot for Maestro (Preview)
- Process Repository
- Implementing a simple BPMN process
- Implementing a complex BPMN process
- Debugging
- Simulating
- Evaluations (Preview)
- Common implementation scenarios
- Building with Maestro Case
- Introduction to Maestro Case
- Maestro BPMN vs. Maestro Case: when to use case management
- The Maestro Case lifecycle: from event trigger to app experience
- Build your first case with Maestro Case
- Build a Maestro Case with a coding agent (preview)
- Defining case keys (system vs. external)
- Establishing task I/O and write-back contracts
- Exit rules and early stage termination
- Modeling primary and secondary stages
- Triggering a case from Data Fabric
- Implementing stage-level personas and permissions
- Setting SLAs and automated escalation rules
- Configuring a rework loop (re-entry)
- Configuring and testing the Case Manager Agent (preview)
- Case Manager input and output contract
- Maestro Case component dictionary
- Building with Maestro Flow
- Maestro Automate
- Integrations
- Operating
- Monitoring
- Optimizing
- Reference information
Process optimization capabilities in Maestro, combining built-in analytics and Process Mining to reveal inefficiencies, delays, and rework patterns over time.
Process optimization helps you analyze how your processes perform over time and turn execution data into improvement opportunities.
It combines Maestro’s built-in analytics with Process Mining insights to reveal inefficiencies, delays, and rework patterns that affect business outcomes.
You can access Process optimization from:
- The Optimize tab inside Maestro, or
- The Process Optimization app in Process Mining, automatically linked to each published process.
Use Process optimization to:
- Visualize end-to-end execution paths. Compare actual process flows with their modeled versions.
- Measure performance trends. Track KPIs such as duration, automation rate, and conformance over time.
- Identify improvement areas. Detect bottlenecks, rework loops, and low-automation steps.
- Simulate changes. Test process adjustments and measure their impact before deployment.
Optimization connects process execution and process intelligence, helping you move from monitoring what happened to understanding why it happened and how to make it better.