Integrating with Jira
Connect Bito's AI Architect with Jira for feasibility analysis, technical design, and cross-repo impact assessment.
Planning is where engineering teams lose the most time. This is where AI Architect starts.
Senior engineers and tech leads spend hours on work that should take minutes, reading through old tickets to understand what went wrong before, figuring out what a one-line Epic description actually requires, mapping out which services will be affected and which ones have been fragile in the past.
AI Architect automates this work inside Jira. When an Epic or Story is created, AI Architect posts a detailed implementation plan directly as a comment on the ticket. Feasibility analysis, story breakdown, risk warnings, historical patterns from your team's own tickets — all generated in minutes, right where your team plans.
What gets posted on your ticket
When AI Architect analyzes an Epic or Story, it produces a structured Markdown implementation plan that covers:
Feasibility assessment — Is this viable given your current architecture? Which services are involved, and how stable are they? What's the blast radius if something goes wrong?
Story breakdown — For Epics, a full decomposition into Stories with acceptance criteria, dependencies, and recommended execution order across sprints.
Effort estimates — Both traditional and agentic estimates, with a breakdown of where AI tooling saves the most time and where it doesn't.
Proactive risk detection — Race conditions in concurrent flows, memory leak patterns, regression-prone areas, API rate-limiting gaps, security concerns. Each risk comes with a suggested mitigation drawn from your team's actual history.
Historical pattern insights — AI Architect references past tickets to flag issues your team has already encountered. For example: "A similar concurrency issue took 3 sprints to resolve in PROJ-456 — consider redesigning the locking strategy before implementing this."
Open questions — Technical decisions that need to be made before implementation begins, so they don't surface mid-sprint.
The plan is in Markdown. Engineers can paste it directly into Cursor, Claude Code, or any other coding agent to start implementation with full architectural context already loaded.
Note: Confluence pages linked to Jira tickets are now automatically pulled as context for plan generation.
Setup
Enable AI Architect for your workspace
AI Architect can be set up using one of the following deployment options:
Bito-hosted
AI Architect is managed and maintained by Bito.
Follow the Bito-hosted installation guide to get started.
If AI Architect isn't already active for your workspace, contact the Bito team at [email protected] to enable it.
Self-hosted
AI Architect is deployed and managed within your own infrastructure.
Follow the self-hosted installation guide to set up and configure AI Architect for your workspace.
Connect Jira
Bito supports two ways to connect with Jira, depending on where your Jira instance is hosted:
Jira Cloud: for Jira sites hosted by Atlassian (e.g.,
https://mycompany.atlassian.net).Jira Data Center: for Jira instances hosted on your own company domain or servers (e.g.,
https://jira.mycompany.com).
Connect Bito with Jira Cloud (hosted by Atlassian)
Navigate to the Manage integrations page in your Bito dashboard
In the Available integrations section, you will see Jira. Click Connect to proceed.
Select the option Jira Cloud. You will be redirected to the official Jira website, where you need to grant Bito access to your Atlassian account.
Click Accept to continue. If the integration is successful, you will be redirected back to Bito.
Connect Bito with Jira Data Center (hosted on your own server)
Navigate to the Manage integrations page in your Bito dashboard
In the Available integrations section, you will see Jira. Click Connect to proceed.
Select the option Jira Data Center (self-managed).
Provide connection details:
Domain URL: Enter the base URL for your Jira instance (e.g.
https://jira.mycompany.com).Personal Access Token: Enter a valid Personal Access Token with admin permissions. Read the official Jira documentation to learn how to create a Personal Access Token.
Click Connect to Jira. You will be redirected to your self-hosted Jira website, where you need to grant Bito access to your Jira account.
Click Allow to continue. If the integration is successful, you will be redirected back to Bito.
Enable the feature flags
Two flags control how AI Architect behaves on your tickets. Contact the Bito team at [email protected] to enable them — a self-serve UI toggle is coming soon.
Jira Analysis Enabled
Trigger on-demand analysis using @bito, /bito, or #bito in comments.
Alternatively, add the labels bito, bito-analyse, or bito-analyze to the ticket.
Jira Auto Analysis Enabled
Automatically triggers analysis when an Epic or Story is created or updated.
Start with Jira Analysis Enabled if you want to try it manually first, then enable auto-analysis when you're ready to run it on all new tickets.
How to trigger AI Architect in Jira
Automatic analysis
When enabled, AI Architect listens for new or updated Epics and Stories and posts the implementation plan automatically, no action needed from the ticket creator.
Requires the Jira Auto Analysis Enabled flag to be enabled for your workspace. Contact the Bito team at [email protected] to enable it.
You can download the plan (markdown file) and use it in your agentic tools (Cursor, Claude Code, etc.) to further update it or use the analysis.
On-demand analysis
Trigger analysis manually on any ticket by using any of the following in a Jira comment:
You can use the @bito, /bito, or #bito in Jira comment to ask Bito to make changes to the analysis or update based on your needs, or add more details to update the plan based on any new information.
Example:
Or by adding one of these labels to the ticket:
Requires the Jira Analysis Enabled flag to be enabled for your workspace. Contact the Bito team at [email protected] to enable it.
Auto triage
By default, when auto-analysis is enabled, AI Architect generates a plan for every new or updated Epic or Story. Auto triage adds a layer of intelligence before that happens: it evaluates whether a ticket actually needs a detailed implementation plan, and skips generating one if it doesn't.
This keeps your tickets clean and avoids generating plans for work that is already well-defined or straightforward enough to implement without one.
Note: Only events from your selected Jira projects are processed. Events from unselected projects are ignored before reaching auto triage.
How auto triage works
When a Story or Epic is created or updated, AI Architect reads the full ticket — title, description, comments, attachments, linked issues, and any associated Confluence pages — and assigns a complexity score from 1 to 10.
Below threshold (default: 7)
Plan is skipped. An optional comment is posted on the ticket.
At or above threshold
Implementation plan is generated as usual.
When a ticket is below the threshold, AI Architect posts the following comment:
AI Architect — This ticket doesn't appear to need a detailed implementation plan based on its scope and complexity. If you'd like one anyway, type @Bito with what you want (example:
@Bito technical plan)
Once a plan or skip notification has been posted on a ticket, auto triage will not re-trigger automatically on subsequent updates. To request a new plan, use @bito, /bito, or #bito directly on the ticket.
Note: AI Architect can perform live web research to incorporate up-to-date technical context into its evaluation and plan generation.
Supported attachment formats
When evaluating a ticket, AI Architect can read attachments in the following formats:
.pdf.docx.xlsx.csv.json.yaml.zip
SVG files are supported as images only. Binary image attachments (.png, .jpg, .gif, .webp) are not yet supported, full image support is coming soon.
Configuration
The following settings control how auto triage behaves for your workspace. Contact the Bito team at [email protected] to configure these.
Complexity threshold
Default: 7
Minimum score required for a plan to be generated.
Auto analysis type
plancomment
Whether to generate a full plan or post a guidance comment when a ticket is created or updated.
What AI Architect knows about your team
AI Architect draws on multiple sources when it analyzes a ticket:
Your codebase — All your repositories, services, API endpoints, modules, and design patterns are indexed into a knowledge graph. AI Architect understands how your system fits together, not just what individual files contain.
Your Jira history — AI Architect analyzes your team's Jira tickets and categorizes recurring patterns: race conditions across subsystems, services with instability histories, security and credential issues, error handling gaps, API rate-limiting problems. This context is applied to every new ticket so your team doesn't repeat what's already been learned.
Other context sources — AI Architect also incorporates insights from Confluence documents, Linear, observability data, Slack messages, and any custom instructions you provide.
Learn how Bito builds the knowledge graph
Agent skills power the Jira integration
AI Architect works through purpose-built agentic skills. The two important agent skills that run in Jira are:
bito-epic-to-plan — Triggered on Epics. Converts an approved Epic or PRD into a sprint-ready plan with multiple architectural approaches, then granular Stories with effort estimates and acceptance criteria.
bito-scope-to-plan — Triggered on Stories. Auto-scales its output — a full multi-workstream plan for complex Stories, a focused single-workstream plan for simpler ones.
Skill selection by issue type
AI Architect uses different skill selection logic depending on the Jira issue type.
Story
Always uses bito-scope-to-plan
Epic
Always uses bito-epic-to-plan
All other types (Bug, Task, Sub-task, etc.)
Dynamically selected based on your comment
For Bugs, Tasks, and other issue types, AI Architect reads the intent of your latest comment and automatically picks the most appropriate skill from its full library. For example, a comment like "is this feasible?" triggers bito-feasibility, while "spike on this approach" triggers bito-spike. If the intent is ambiguous, AI Architect falls back to bito-scope-to-plan.
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