One shell. Every role on your team.
Whether you're a tech lead enforcing quality, a solo developer shipping fast, or a security officer proving compliance — Fabriq adapts to how your team works with AI agents.
Tech Lead
Use case 1Review AI-generated code with confidence
The challenge
Your team uses AI agents to move faster, but you're the one accountable for what lands in the repo. Without visibility into what the agent changed and why, code review becomes guesswork. One bad capability call can cross-contaminate your workspace assets.
How Fabriq helps
Fabriq surfaces every agent action as an explicit confirmation prompt. You review the target, payload, and expected effect before anything touches disk. Every decision leaves a sourced, validated audit trail — so when you approve, you approve with evidence, not blind trust.
Solo Developer
Use case 2Ship fast without the fear of what the agent broke
The challenge
You use Claude Code or OpenCode to accelerate every task, but without a team to catch mistakes, one rogue agent action can corrupt hours of work. You need the speed of AI assistance without giving up control of your local environment.
How Fabriq helps
Fabriq lets you see exactly what the agent is about to do before it happens. Reversal is a first-class operation — not a post-mortem. The two-dimensional workspace ensures your long-lived assets stay separate from short-lived feature worktrees, so you can experiment freely without risk.
Product Manager
Use case 3Turn specs into shippable features with full visibility
The challenge
You write product specs and hand them to engineers who use AI to implement them. What comes back is a black box — you can't tell what was thoughtfully designed vs what the LLM hallucinated. The feature lifecycle is invisible until the PR lands.
How Fabriq helps
Fabriq's Feature Workbench makes the entire lifecycle visible: from spec to solution design to implementation to validation. Each stage produces structured artifacts with evidence. You can see what decisions were made, where the code came from, and whether it matches the spec — without needing to read every diff.
Security & Compliance
Use case 4Prove every AI-generated change is auditable
The challenge
AI-generated code is a compliance nightmare. No provenance, no audit trail, no way to prove what the agent did and why. Regulators and auditors won't accept 'the LLM wrote it' as an answer. You need deterministic guarantees, not probabilistic assurances.
How Fabriq helps
Fabriq's capability allowlist prevents unauthorized operations before they execute. Every agent action passes through a typed, validated IPC channel with full audit logging. Cognitive artifacts are revalidated by a deterministic engine before they can be committed — if the artifact doesn't match the source, it doesn't get in.
Open Source Maintainer
Use case 5Keep quality high when AI writes the PRs
The challenge
Contributors use AI tools to write code, but quality is inconsistent and reviewing AI-generated PRs is time-consuming. You need a way to enforce project standards without dictating what tools your contributors can use — and without adding friction to the contribution process.
How Fabriq helps
Fabriq's scanner+validator pipeline extracts rules from your codebase and revalidates every artifact against its source. The agent-agnostic ACP protocol means contributors can use any compatible agent. Deterministic validation runs the same checks every time — no LLM variability, just consistent quality gates.
Platform & DevOps Engineer
Use case 6Build the AI toolchain your org can trust
The challenge
You're responsible for the engineering toolchain. AI integrations need to be auditable, extensible, and secure — not a wild west of prompt injections and silent mutations. You need a platform you can build on, not another tool you have to police.
How Fabriq helps
Fabriq's plugin manifest system lets you register routes, panels, and agent backends through a validated schema. Typed IPC means every capability call is checked at the protocol level before execution. The ACP stdio JSON-RPC bridge works with any compatible agent — no PTY shims, no security holes.
Three ideas that serve every role
No matter your role, the same three design principles make Fabriq valuable for AI-driven development.
Separation of concerns
Workspace assets and feature worktrees live on separate dimensions. The agent can never accidentally overwrite your source of truth.
Provenance by default
Every change carries evidence — sourced, validated, attributable. You know not just what changed, but why and where it came from.
Human in the loop
Agents propose, you dispose. Every destructive action surfaces as an explicit confirmation prompt. Nothing touches disk without your say-so.