Jira + governed AI agents
Jira is where the work is described, badly, by people in a hurry. An agent that can read the surrounding context and propose a better-formed issue is genuinely useful, provided it cannot quietly reorganise your backlog.
Connecting Jira
| Definition | Import the Jira Cloud REST API specification for issues, projects and search. |
|---|---|
| Credential | API token with a user email, or OAuth 2.0 for a marketplace app. |
| Stored as | A secret:// reference to your own vault. FlozentAi never holds the value. |
Actions teams usually publish
Action names are yours to choose during review — these are the shapes most teams end up with. Anything that writes is confirmation-gated unless you deliberately publish it otherwise.
| Action | Kind | What it does |
|---|---|---|
issue.get | Read | Read an issue with comments. |
issue.search | Read | Run a JQL query. |
issue.create | Write | Create an issue from a prepared description. |
issue.comment | Write | Add a comment after approval. |
What people build with it
- A support-to-engineering handoff that drafts the bug report with reproduction detail attached.
- A standup assistant that summarises what moved, read-only.
- An intake agent that turns a customer request into a well-formed issue for a human to file.
There is no field in the console that accepts a token or a password. You store the secret in your vault and give FlozentAi a reference; the runtime resolves it inside the execution layer, after the policy decision.
Jira questions
Can we scope it to one project?
Import a specification limited to what you want reachable, and use a Jira-side account whose permissions match. FlozentAi enforces what was published; the provider still enforces what the credential can do.
Use cases that involve Jira
Connect Jira the governed way.
Import the specification, review what it found, and publish only what you want reachable.