Bangun dengan AI agent Anda
Build a NocoBase app — Issue Tracking: a tree table with multi-select facets and checkbox columns. Match the layout and signature visuals of this reference prototype: https://static-docs.nocobase.com/solution/templates/15-issue-tracking.html
Sebelum memulai, ikuti panduan cepat AI agent untuk menginstal NocoBase dan menghubungkan agent Anda. Hasil AI dapat bervariasi; bergantung pada model dan kompleksitas sistem, mungkin diperlukan penyesuaian atau beberapa putaran tambahan.
Introduction
Use your favorite AI agent and NocoBase to quickly build a customizable, reliable, and continuously evolving issue tracking system for managing projects, epics, issues, sub-tasks, statuses, priorities, assignees, and issue IDs.
You can copy the prompt below and let your AI agent generate the basic structure of the issue tracking system in NocoBase, then fine-tune fields, pages, task hierarchies, and workflows through the no-code UI.
This system is a great fit for software development, product R&D, infrastructure, API, frontend, and technical support teams that need a unified way to manage bugs, technical tasks, improvement requests, and cross-project issues.
Issue workspace with multi-dimensional filtering:
AI employee generating an issue analysis report:
Analyzing issues by priority and status:
What problems does an issue tracking system solve?
Issue tracking gives the team a clear picture of which project an issue belongs to, how it is progressing, how urgent it is, who owns it, and how it relates to other tasks in the hierarchy.
When issues are scattered across chat threads, documents, and multiple project tools, teams easily run into duplicate submissions, unclear ownership, urgent issues stuck for too long, and epics disconnected from the actual execution tasks.
The system supports statuses such as Todo, In Progress, In Review, Done, Backlog, and Cancelled, as well as priorities like Urgent, High, Medium, Low, and None. The team can quickly locate the issues that need attention through the left-side filters or the status tabs at the top.
Each issue record can also display its assignee and a unique issue ID, making it easy for the team to reference issues precisely in discussions, code commits, or retrospectives.
An AI team lead can analyze the content of each issue, identify the skills required, and recommend the right employee to take it on. An AI data analyst can also read all issue data and generate reports on completion rates, active issues, urgent issue risks, and assignee workload.
Core features
Project and issue management
- Unified multi-project management: Centrally manage issues across projects such as Core Platform, API Gateway, Frontend, and Infrastructure.
- Essential issue information: Record the issue title, ID, project, status, priority, assignee, and parent epic.
- Quick project filtering: View issue lists and counts by project to quickly locate pending items for different teams.
Epic and sub-task hierarchy
- Hierarchical task management: Use epics to organize large requirements and link bugs, tasks, and sub-issues under them.
- Expand and collapse views: Expand or collapse sub-tasks on demand to switch quickly between the overall goal and the concrete execution items.
- Execution progress tracking: Combine the statuses of parent and child issues to see how work under each epic is progressing.
Status and priority management
- Complete issue statuses: Support stages such as Backlog, Todo, In Progress, In Review, Done, and Cancelled.
- Multi-level priorities: Use Urgent, High, Medium, Low, and None to distinguish how pressing each issue is.
- Fast risk identification: Use status and priority tags to spot urgent issues that are unfinished or have stalled for a long time.
Issue workspace and filtering
- Multi-condition filtering: Filter issues by project, status, priority, and assignee.
- Quick issue actions: Create, delete, and refresh issues, and expand or collapse the entire task hierarchy.
- Unified issue list: View issue titles, statuses, priorities, assignees, and issue IDs in a single workspace.
AI-powered assignment
- AI analyzes task requirements: Determine the skills needed to complete a task based on the issue title and content.
- AI recommends assignees: Recommend the right person by combining employee capabilities with current workload.
- AI-assisted task coordination: Spot unassigned or poorly assigned issues and suggest adjustments.
AI issue and risk analysis
- Issue progress analysis: Track total issue count, completion rate, and the number of currently active issues.
- Urgent issue identification: Analyze how Urgent issues are progressing and locate high-risk tasks still in progress.
- Workload and assignment analysis: Compare the number of issues each assignee owns and identify unassigned issues.
- Risk summary generation: Automatically summarize the project’s current major risks, blockers, and top priorities.
Why build an issue tracking system with AI and NocoBase?
Once an issue tracking system is actually used by an engineering team, it has to handle more than titles and statuses — it also covers projects, epics, sub-tasks, priorities, assignees, and data analysis.
If you generate a task list from scratch with plain vibe coding, you can usually get CRUD done quickly, but hierarchical tasks, compound filtering, assignee management, data permissions, and analysis reports still require ongoing development.
NocoBase can connect projects, issues, epics, sub-tasks, and people data, and present them in a unified way through tree tables, filters, status tags, and priority tags. The team can also add new projects, statuses, or priority rules to match their actual development process.
AI can go further by participating in task assignment and data analysis. An AI team lead can understand issue content and recommend the right person based on employee skills; an AI data analyst can read issue data and automatically summarize completion rates, risks, priority distribution, and team workload.
An issue tracking system built this way is not just a static task table — it is an internal system that supports complex engineering collaboration, continuous analysis, and long-term iteration.
FAQ
- Can I quickly filter issues by project, status, and priority?
Yes. The system supports filtering issues by projects such as Core Platform, API Gateway, Frontend, and Infrastructure, and combining filters across statuses like Backlog, Todo, In Progress, In Review, Done, and Cancelled, as well as priorities like Urgent, High, Medium, Low, and None.
Each filter option can also show the corresponding issue count, helping the team quickly see which project or stage most issues are concentrated in.
- Can I manage the hierarchy between epics and sub-tasks?
Yes. Complex requirements can first be created as epics, with bugs, technical tasks, or improvements linked underneath as sub-tasks.
The workspace supports expanding and collapsing the hierarchy, so the team can see the overall goal while also tracking the status, priority, and assignee of every execution item.
- Can I customize issue statuses to match my team’s process?
Yes. The system can use statuses such as Backlog, Todo, In Progress, In Review, Done, and Cancelled, and you can add stages like In Testing, Ready to Release, or Blocked based on your team’s actual process.
When your development process changes, you can keep adjusting statuses, page filters, and workflows without rebuilding the entire system.
- Can I set priorities for issues and identify high-risk tasks?
Yes. Every issue can be set to Urgent, High, Medium, Low, or None, clearly displayed with distinct tags.
The team can combine status and priority to identify high-risk tasks, such as urgent issues still sitting in In Progress, or high-priority issues that have not entered processing for a long time.
- Can AI recommend the right assignee based on issue content?
Yes. An AI team lead can analyze the issue title, description, and project to determine the technical skills the task requires, then recommend the right person based on employee skills and current workload.
This reduces manual assignment time and helps the team see whether tasks are concentrated on a few members.
- Can I see how many issues each member is responsible for?
Yes. The system can count issues by assignee and further break them down into completed, in-progress, and pending tasks.
AI can also identify uneven workload distribution, issues with no owner, and which members are carrying more urgent or high-priority tasks.
- Can AI analyze issue completion rates and engineering risks?
Yes. AI can read issue status, priority, project, and assignee data to calculate total issues, completion rates, and the proportion of active issues, and identify tasks that need attention.
For example, the analysis report in the screenshot can reveal a low completion rate for Urgent issues, multiple urgent tasks still in In Progress, and whether there are currently unassigned issues.
- Can issue analysis reports be previewed and exported?
Yes. Once AI generates a report, it can be viewed as Preview, Markdown, or HTML.
Reports can also be downloaded as Markdown or HTML, or printed as PDF — useful for engineering weekly meetings, project retrospectives, and management reporting.
- Can I control which issues different members can view and edit?
Yes. NocoBase supports permissions by role, project, assignee, and action type.
For example, regular members can only update issues assigned to them; project leads can manage issues and assignees within their projects; team leads can view cross-project workload; and administrators can configure fields, statuses, priorities, and permissions.
This fits real engineering collaboration far better than letting everyone edit every issue.
- Can I track who modified an issue?
Yes. You can enable action history and audit logs on demand to record changes to issue status, priority, assignee, and other fields.
When an urgent task is delayed, cancelled, or reassigned, the team can trace who made the change and when, making retrospectives and accountability much easier.
- Can Claude Code, Codex, Cursor, or OpenCode help build the issue tracking system?
Yes. AI coding agents such as Claude Code, Codex, Cursor, and OpenCode can connect to NocoBase and generate the project table, issue table, epic and sub-task relations, filtering pages, and analysis features from a prompt.
Once the basic system is generated, the team can keep adjusting the data structure, pages, statuses, and permissions through NocoBase’s no-code UI, without asking AI to rewrite the app from scratch for every change.
- How is this different from a task list generated by plain vibe coding?
Plain vibe coding can quickly produce an issue list or kanban board, but when it comes to epic hierarchies, compound filtering, fine-grained permissions, action history, workload analysis, and long-term maintenance, it usually requires writing more and more code.
NocoBase already provides the data relations, permissions, workflows, auditing, and extensibility a business system needs. AI accelerates the build and the analysis, while NocoBase carries the continuously running engineering process.
- Is this issue tracking system suitable for long-term use by an engineering team?
Yes. After launch, you can keep adding projects, issue types, statuses, priorities, analysis metrics, and automated workflows, and adjust permissions as the team grows.
Enterprises can also enable single sign-on, notifications, APIs, action history, and plugin extensions as needed. Compared with a one-off task management demo, it is far better suited for building a maintainable, traceable, and continuously evolving issue tracking system.
