๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ถ๐ฟ๐ฎ ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐๐ถ๐๐ต ๐ ๐๐ฃ ๐ฎ๐ป๐ฑ ๐ง๐ฒ๐บ๐ฝ๐ผ๐ฟ๐ฎ๐น
Most AI demos fail when tasks take time. A crash means you lose all progress. Business work needs reliability.
I built an open source platform to fix this. It uses MCP and Temporal.
MCP manages tool access for Jira and Confluence. It creates a clean tool surface. You add tools without changing agent logic. This keeps prompts focused on intent.
Temporal ensures durable execution. It saves every step. If a worker dies, the work resumes from the last completed step. It handles retries and timeouts. Human approvals become a normal part of the flow.
This setup adds more infrastructure. It is too much for short tasks. It pays off for systems needing high reliability.
The stack includes:
- MCP layer for Atlassian tools.
- Temporal workers for workflows.
- Webhook gateway for Jira events.
- Streamlit UI for inspection.
- Support for OpenAI, Anthropic, Gemini, and vLLM.
Split intent, reliability, and tool boundaries. This makes the system easier to test. Keep non-deterministic code in activities. This makes debugging simple.
How do you handle long agent workflows? Do you use Temporal or a custom loop?
Source: https://dev.to/ahmetozel/building-an-agentic-jira-automation-platform-with-mcp-and-temporal-1521 Optional learning community: https://t.me/GyaanSetuAi