Inspiration
Customer support teams spend too much time on repetitive tasks like triaging, routing, and responding to tickets. We wanted to explore how multiple AI agents, orchestrated through UiPath Maestro, could work together like a real support organization to automate this process while keeping humans in the loop when needed.
What it does
Maestro SupportIQ automates the customer support lifecycle. It classifies incoming tickets, generates AI-powered resolutions, intelligently routes complex cases to specialized AI agents, performs a final quality review, and turns every resolved ticket into a reusable knowledge base article for continuous learning.
How we built it
We built the solution using UiPath Maestro, BPMN workflows, and UiPath Agent Builder. The workflow orchestrates multiple low-code AI agents—including triage, resolution, routing, specialist, review, and learning agents—through a confidence-based decision process.
Challenges we ran into
The biggest challenge was designing reliable multi-agent orchestration while maintaining consistent data flow between agents. We also encountered limitations with preview-stage Maestro features, particularly around Action Apps, and adapted the workflow to preserve the intended human review experience.
Accomplishments that we're proud of
We're proud of building a complete end-to-end AI support workflow that goes beyond simple ticket resolution. The solution demonstrates intelligent orchestration, specialist AI collaboration, quality assurance, and continuous knowledge generation within a single Maestro process.
What we learned
We learned that effective AI automation isn't about a single powerful model—it's about orchestrating specialized agents with clear responsibilities. We also gained hands-on experience designing enterprise-grade workflows using UiPath Maestro and BPMN.
What's next for Maestro SupportIQ
Next, we'd integrate enterprise knowledge bases and CRM systems, enable real-time retrieval of historical resolutions, add analytics dashboards and SLA monitoring, and allow the Learning Agent to continuously improve future ticket resolutions using accumulated organizational knowledge.
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