For teams building with AI agents
Write the spec. Coordinate every AI coding tool. Ship real software.
Spec-native Agentic Development Environment
fabriqa.ai turns product intent into versioned specs, routes work across your AI coding tools, and keeps humans in the loop from plan to review in one interface.Coordinate Claude Code, Codex, GitHub Copilot, Cursor, Kiro, and OpenCode in one reviewable delivery workflow.
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Claude Code
Codex
GitHub Copilot
Cursor
Kiro
OpenCodeThe product
One workspace. Spec-native. Every agent.
- 01Intent captured
- 02Work routed
- 03Review against spec
AI made implementation fast. Alignment is now the bottleneck.
A prompt can become a pull request in minutes. That speed only helps when the team agrees on what should be built before agents start changing code. Requirements, constraints, acceptance criteria, and repo context become managed work, so agents can plan, implement, review, and loop against the same shared spec.
This is not a code generation problem. It is an alignment and delivery workflow problem.
Learn the system
The three pillars of the fabriqa.ai ADE.
- 01Spec-native development.Turn product intent into explicit specs, tasks, acceptance criteria, and review context that every agent can read.
- 02Provider-open AI coding tools.Claude Code, Codex, GitHub Copilot, Cursor, Kiro, OpenCode, ACP agents, and BYOK models in one workflow with shared specs and shared history.
- 03Reviewable agent delivery.Spec-backed work across agents with visible handoffs, human approvals, permissions, and cost visibility.
Why spec-native
Prompts drift. Specs hold.
Prompt history is not team alignment.
Local agent plans disappear into one person's terminal, editor, or chat. fabriqa.ai anchors execution to explicit specs, tasks, and acceptance criteria so product intent survives every handoff between people, agents, providers, and days.
Markdown-file specs don't scale to a team.
Frameworks like specs.md, SpecKit, BMAD-METHOD, and Superpower target one developer and one repository. Real teams have PMs, architects, multiple engineers, and 32-repo workspaces. fabriqa.ai stores specs as entity artifacts in a workspace as the source-of-truth tracker, not as markdown files in a Git repo. It can export them with connectors to markdown files, Jira, and Linear.
Review is too late to discover the team was never aligned.
Every task, handoff, review, and diff traces back to original intent. No copy-paste between agents, no context loss between providers, no invisible plan hiding behind a pull request. PMs review the spec; architects comment; agents execute; engineers ship.
How it works
From spec to shipped code in three steps.
Spec
Define what needs to be built. Once.
Write requirements, constraints, and acceptance criteria as a versioned, reviewable spec. Routing decisions and handoffs live in one document so agents never improvise.
Route
Split spec-backed tasks across agents.
A coordinator turns the managed spec into a loop: planner agents clarify ambiguity, developer agents implement, reviewer agents check the work against the spec, and humans approve the result. Claude Code, Codex, GitHub Copilot, Cursor, Kiro, and OpenCode can each back the roles your team trusts.
Review
Approve what matches the spec.
Inspect every change against the original intent. Permissions, cost visibility, and provider context are unified, so reviewing AI work feels like reviewing a pull request — not chasing prompts.