AI agents write better code when they have better context. FlowBoard gives them structured requirements, bug details and acceptance criteria so the output is actually right.
Four steps from backlog to shipped code. No babysitting required.
The quality of your task context determines the quality of the agent's output. FlowBoard gives you structured fields that matter: bug repro steps, stack traces, environment info, acceptance criteria, file hints, and affected components. This is what makes FlowBoard different from "just use GitHub Issues."
AI agents connect via FlowBoard's API or MCP server. They pick the highest-priority unassigned task and move it to "In Progress."
The agent writes code, runs your test suite, pushes a branch, and creates a pull/merge request. It comments progress directly on the FlowBoard task.
You wake up to PRs ready for review. Approve, merge, and FlowBoard auto-closes the task. Repeat.
These are workflow patterns, not built-in features. FlowBoard's API supports any agent workflow - trigger them from Claude Code, Cursor Automations, CI pipelines, or your own scripts.
Picks feature and fix tasks, writes code, creates branches and PRs. Follows your codebase conventions.
Runs test suites, writes missing tests, validates acceptance criteria. Updates QA status on the task.
Reviews PRs for code quality, security, and adherence to standards. Adds comments and approves.
Analyzes bug reports with structured context - repro steps, stack traces, environment. Writes targeted fixes.
Every feature designed so AI agents can hit the ground running.
66% of developers say AI generates "almost right" code. The reason? Insufficient context. FlowBoard's structured task fields - bug reproduction steps, acceptance criteria, affected components, related code hints - give agents the full picture. Better context = better code.
Custom priority formulas ensure agents work on what matters most. No manual assignment needed.
Agents move tasks through statuses, add comments, and log time. You see progress in real-time.
GitHub and GitLab webhooks auto-link PRs to tasks. Tasks auto-close when PRs merge.
Native Model Context Protocol support. Claude Code and Cursor connect directly to your board.
14+ REST endpoints with API key auth. Build any agent workflow you need.
Imagine a world where you define what to build, and AI agents execute it. Not a demo. Not a prototype. A real, production-ready workflow where you write your backlog at night, and wake up to pull requests, test results, and a board that moved forward while you slept.
FlowBoard is built for this future - today.
Free plan. No credit card. Set up in 5 minutes.
For agents, the difference between a vague prompt and a detailed specification.
Any agent that can make HTTP calls. FlowBoard has a REST API with 14+ endpoints and an MCP server for Claude Code and Cursor. You can also build custom agents using the API.
No. FlowBoard works the same way for human and AI team members. Create tasks, set priorities, and let agents pick them up - just like a human dev would.
Agents can add comments to tasks asking for clarification, just like a real developer would. You can also set tasks to "blocked" status with a reason.
FlowBoard never sees your code. Agents run in your infrastructure, connect to your repos, and use FlowBoard only for task management.
Absolutely. That's the whole point. Some tasks go to agents, others to humans. FlowBoard treats them the same.
Vibe coding means describing what you want and letting AI build it. FlowBoard is the project management layer for vibe coding - you describe tasks with rich context, AI agents build them, and the board tracks everything automatically.
Linear recently added AI agents that create and triage issues. FlowBoard takes a different approach - instead of AI managing your board, FlowBoard gives AI agents the structured context they need to write better code. Both can work together: Linear for triage, FlowBoard for execution context.
Join teams using FlowBoard to turn their backlog into working code with AI agents.