Nx doesn't just make monorepos manageable. It makes AI agents effective.
AI agents have made massive inroads in enterprise software development in a short amount of time. With predictions that AI spend will continue to increase in 2026, has the return on investment materialized with increased budgets? Many organizations are saying “No,” with reports that only 25% of AI initiatives have delivered the expected ROI.
So why are AI initiatives lagging behind the massive investment being made into them? It’s not the agents, it’s the architecture. AI agents underperform because polyrepos blind them to cross-project context and force manual coordination. Nx fixes this, enabling major productivity gains and unlocking work teams previously avoided entirely.
Monorepos vs Polyrepos
Polyrepos isolate projects by having them in separate repos. Sharing code between projects is done by publishing packages and depending on them
Monorepos have multiple projects inside a single repo. This enables projects to depend directly on one another rather than going through an external package manager.
AI Agents Are Underperforming in Large Organizations
Why are so few AI initiatives delivering results at the Enterprise level? Enterprise software projects present unique challenges. They tend to be highly complex and spread across hundreds of repositories in an organization. Different products and teams rely on code and services from other repos.
This polyrepo approach naturally limits the context available to AI agents at any one time.
No cross-project context
In a polyrepo, an AI agent sees one project at a time, blind to how changes ripple across the system. Change a UI component library, and the agent has no idea which applications consume it or how they'll break. In an Nx workspace, the agent sees the whole codebase. It understands dependencies, proposes consistent changes, and catches downstream impacts before they become bugs.
No way to land cross-project changes atomically
When a change touches multiple repos, you're stuck coordinating separate PRs, managing backwards compatibility, and sequencing merges carefully. The agent might do the coding work, but humans still carry the cross-project cross-team coordination burden. With Nx, an agent can modify 20 projects in a single PR. One review, one approval, one merge. No version drift, no compatibility gymnastics.
Repo boundaries isolate contexts (aka the "Memento Problem")
Every repo boundary wipes the agent's context. You update Repo A with Claude on Monday, then open Repo B on Thursday and try to re-explain what you did and why. Even with a system, you're reconstructing intent from breadcrumbs, and the agent is guessing at gaps. With Nx, the agent works in one continuous context. It might still make mistakes, but they're coherent mistakes that are easier to spot and fix.
Getting 30% Productivity Boost
Cross-project and cross-cutting changes make up only up to 20% of commits, but they're disproportionately expensive. They're larger, more complex, and can drag on for months or years.
AI agents excel at exactly this kind of work, but only if they are in the right environment. For instance, Airbnb compressed an 18-month migration down to 6 weeks using agents in a monorepo.
This is possible because AI agents can scan and update large volumes of code in a predictable way. A trivial change to a library that affects its clients can take multiple weeks in a polyrepo and just a day with an Nx monorepo.
Here's a side-by-side demo comparing the time spent on a cross-project change in a monorepo and a polyrepo:
Even in this small example, a developer using Claude Code in an Nx workspace completed their work 4x faster with far fewer interruptions.
In real organizations, where coordination costs are higher and delays (like code reviews, approvals, and context switches across teams) compound, the gains are significantly higher.
Because different tasks benefit from Nx to varying degrees, improvements typically look like this:
That's a 1/3 reduction in total effort.
Large non-local changes like migrations and refactorings can be largely automated. Smaller ones requiring deep application work become more efficient.
Unlocking Work That Never Happened
The 1/3 gain understates the real impact. In polyrepo environments, cross-project work is so painful that teams avoid it. They duplicate code instead of sharing it. They defer refactors. They let inconsistencies accumulate. The friction isn't just slowing work down. It's preventing work from happening at all.
Nx removes that friction. Suddenly, large-scale refactors, API migrations, and shared library upgrades become feasible. AI agents can propose sweeping improvements that would have been unthinkable before. The productivity gains compound as organizations unlock work they'd previously written off as too expensive.
AI Agents as Differentiator
AI agents will only get more capable. The organizations that pull ahead won't be the ones with marginally better models. They'll be the ones whose infrastructure lets agents operate at full potential.
This is an architectural moat. Companies running polyrepos are fighting with one hand tied behind their back. Every competitor that adopts Nx gains a structural advantage that widens over time as agents improve.
The question isn't whether AI agents will become a differentiator. It's whether your architecture is ready to amplify them.