Design your
AI workflow.
Describe any business process or daily task, and this tool will design a complete AI agent workflow for you — triggers, skills, MCP connectors, and outputs. A ready-to-build blueprint in 2 minutes.
No coding required. Tell the AI what you want to automate, answer a few questions, and get an actionable implementation plan you can build on Tulsk or hand to your team.
How do I automate a business workflow with AI?
Start by identifying a repeatable process with clear inputs and outputs — like customer support triage, weekly reporting, or content review. Then connect your tools via MCP connectors, define skills (the rules the AI follows), set triggers (time-based or event-based), and let the agent runner execute. Platforms like Tulsk bundle all of this into a single workspace.
What is an MCP connector?
MCP (Model Context Protocol) connectors are standardized integrations that let AI agents read from and write to your existing tools — GitHub, Slack, Notion, Shopify, databases, and more. They give AI agents the ability to take real actions in your stack, not just generate text.
What AI tools can automate my work?
AI agent platforms like Tulsk combine skills (captured SOPs), MCP connectors (tool integrations), agent runners (execution engines), and eval engines (quality checks) into workflows that run autonomously. Unlike simple chatbots, these agents can take actions across your entire tool stack on a schedule or in response to events.
How long does it take to set up an AI workflow?
Simple workflows (like automated reporting or ticket triage) can be set up in a few hours on Tulsk. More complex multi-step workflows with custom skills and multiple MCP connectors typically take 1-2 days. Riggd's Harness Setup service configures everything in a fixed 2-week engagement.
What workflows can AI agents handle?
AI agents handle workflows with clear rules and repeatable steps: code review, customer support triage, internal reporting, content production, on-call runbook execution, data processing, CRM updates, and documentation maintenance. The key requirement is well-defined inputs, consistent logic, and measurable outputs.