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Introducing Fabric: The foundation for AI-native engineering

Fabric gives engineering leaders a single, live view of every service's engineering standards, ownership, documentation, test coverage, CI/CD, security controls, and connects that information directly to their AI tools.

Today, we announced Fabric, a new product that gives engineering organizations the foundational context layer their AI tools need to deliver real productivity gains. Fabric provides a live catalog of every service’s engineering standards (ownership, documentation, test coverage, CI/CD, security controls) and connects that information directly to AI coding assistants and agents.

Engineering organizations have invested heavily in AI coding tools over the past two years, but the returns have been uneven. DX’s recent research across 400+ companies shows that while AI adoption is up 65%, engineering velocity has increased by only about 8% at the median. The gap between investment and impact is widening, and the reason isn’t the tools themselves.

Our data points to a foundational problem: AI amplifies existing engineering practices, both good and bad. Teams with clear ownership, strong documentation, and reliable CI/CD see real productivity gains. Teams without those foundations find that AI exacerbates problems—generating code that doesn’t fit the environment, missing dependencies, and creating rework and tech debt. AI is only as effective as the context it has, and for many organizations, that context is fragmented, outdated, or missing.

Fabric addresses this gap by combining three capabilities:

  1. A live, AI-connected service catalog. Fabric automatically ingests data from across the SDLC to maintain a single source of truth for service ownership, health, dependencies, and standards. That architecture-aware context feeds directly to AI assistants and agents via the DX CLI, so they write code that actually fits the environment.

  2. Self-healing scorecards. Scorecards highlight exactly where each service falls short of engineering standards. Teams can then use the DX CLI to point AI agents at failing checks. Agents understand what’s wrong and automatically cut PRs to bring services back into compliance.

  3. AI Readiness validation. Pre-built AI Readiness Scorecards confirm whether services have the security controls, automated tests, and deployment processes needed to scale AI safely—showing, at a glance, which services are ready for autonomous AI workflows and which still require human review.

     

    “We're excited to use Fabric as the foundational context layer our AI tooling needs to actually work well in our environment.”
    Angelo Pace, Head of Platform, Teamworks

The ceiling on AI productivity gains isn’t about the tools; it’s more the engineering foundations those tools depend on. Organizations that capture the next wave of AI-driven productivity will be the ones that invest in those foundations systematically, not the ones that buy another coding assistant and hope for the best.

To learn more about Fabric, request a demo.