Jarvis Registry: Build Governed AI Workflows on a Visual Canvas
Enterprise AI has moved past single-agent question answering — teams now need agents, tools, knowledge sources, MCP servers, and business systems working together across multi-step processes. Without an orchestration layer, these workflows become fragmented, hard to govern, and difficult to monitor. Jarvis Registry Workflows gives teams a visual canvas to design, run, and monitor these agentic processes, turning scattered automation attempts into governed, repeatable AI workflows.
The demo walks through an IT help desk ticket triage workflow built entirely on the Jarvis Registry canvas. When a request arrives by email, Jarvis Registry analyzes intent, queries a knowledge base MCP to pull relevant Confluence content, and evaluates a confidence score. High-confidence cases automatically pull a response template from Google Drive, draft a reply, and send it through the Google Workspace MCP server — grounded in real documentation rather than generated from scratch.
When confidence is low, Jarvis Registry routes the ticket to the IT support team and generates a Jira ticket for manual review instead of guessing at an answer. The same pattern extends beyond IT support to insurance claim review and healthcare pre-authorization — any process with multiple steps, multiple systems, and a judgment call in the middle. Jarvis Registry supports approval gates, conditional branches, parallel fan-out, routing, loops, and agent pools for complex organizational processes.
Enterprise AI now requires agents, tools, knowledge sources, and business systems working together across multi-step processes. Without an orchestration layer, these workflows become fragmented and hard to govern.
An IT help desk request arrives by email, and Jarvis Registry analyzes the request to understand the user's issue and intent before calling a knowledge base MCP to retrieve relevant Confluence information.
The workflow evaluates a confidence level from the retrieved knowledge. High confidence triggers an automated response; low confidence routes the ticket to IT support for manual review.
A live example shows the workflow triggering automatically, finding reliable Confluence information, pulling a response template from Google Drive, and sending a grounded reply email to the customer.
A second example shows the confidence check failing, so Jarvis Registry routes the request to IT support and automatically generates a Jira ticket for manual review.
The same orchestration pattern applies to insurance claim review, healthcare pre-authorization, and any process combining multiple steps, systems, and judgment calls, using logic like approval gates, parallel fan-out, and loops.
