Most "AI automation" demos fall apart the moment a workflow needs to run longer than a single request. An agent makes a few tool calls, the process crashes or times out, and you lose all state. I wanted something that could drive real, multi-step work inside Atlassian (Jira and Confluence) and survive restarts, retries, and failures. So I built an open-source platform around two ideas: MCP for tool access and Temporal for durable execution.
Repo: https://github.com/ahmet-ozel/atlassian-ai-workflow-platform
The problem with one-shot agents
A typical agent loop looks like: read a ticket, decide on an action, call a tool, repeat. This is fine for short tasks. It breaks down when a workflow spans minutes or hours, depends on external systems that fail intermittently, or needs to be resumed after a deploy. If your orchestration lives in a single Python process, any crash means you start over. For business workflows that touch real Jira issues, that is not acceptable.
Why MCP for tools
