If 2025 was the year of agents, 2026 is the year of autonomous workflows. We started with AI Chats (copy and pasting), moved to agentic tools like Claude Code and Cursor (edit files, run commands, but still babysitting), and are now heading toward agents that perform large chunks of work completely autonomously.
AI agents no longer just autocomplete a line or a function body. They can operate independently across projects. This shift will change how we work. Organizations that adapt quickly, and have the right tooling in place, will have a significant advantage.
CI Breaks Autonomy - How Nx fixes it!
We wrote about autonomous AI Agents at scale and the potential barriers organizations might hit when adopting them. One such aspect where many current setups hit a wall is CI.
The local agent implements everything, pushes to CI, and waits. CI fails? You get pulled back in. Context is lost. This disconnect between local development and CI kills full autonomy.
Nx is modular and can be adopted incrementally, but each piece fits seamlessly together. The ci-monitor skill is one such piece.
Skills are portable, shareable agent capabilities that extend what your AI coding agent can do. They work across different AI agents and can be shared via agentskills.io.
When you have your Nx workspace connected to Nx Cloud, the CI Monitor skill opens a communication channel between your local agent and the Nx Cloud CI run via the Nx MCP server. Your local agent can now:
- Monitor CI pipeline status in real-time
- Receive failure information with full context
- Communicate with Nx's Self-Healing CI agent
- Apply verified fixes automatically
- Keep iterating until CI is green
The disconnect is bridged.
Let The Agent Handle the Annoying Part
Fully autonomous AI agents have gained popularity recently, with "Ralph Wiggum loops" being one implementation pattern: autonomous cycles that keep iterating until a task is complete.
Ralph Wiggum loops are autonomous agent workflows where the agent keeps working on a task until completion, without human intervention. The pattern was popularized by Geoffrey Huntley and has become a common approach for running AI agents on well-defined tasks.
In the video demo, a Ralph loop picks up a well-defined user story from a PRD and implements it autonomously:
- The local agent reads requirements and implements the feature
- Runs local quality checks: type checking, linting, testing
- Creates a PR and starts monitoring CI
When CI fails, the Nx Self-Healing CI agent kicks in. It classifies the failure, proposes a verified fix. The local agent sees this through the ci-monitor, applies the fix, pushes again. Another failure (end-to-end test). Same process. Back and forth until CI is green.
Then you get notified and you review the PR. No interruptions during all this CI back and forth.
The competitive advantage goes to organizations ready to adopt these workflows.
But you don't need to go all-in on full autonomy necessarily. You might prefer interactive collaboration. You pair with the agent on implementation, make decisions together, iterate on the approach. When you're 90% done, you hand off. The agent creates the PR, monitors CI, applies fixes, and notifies you when CI is green and is ready for review.
How to Get Started
Setting up CI monitoring requires two steps:
1. Configure AI Agent Support
❯
nx configure-ai-agents
This sets up your workspace with the MCP configuration and skills that enable agent-CI communication.
2. Use the CI Monitor
Leverage the ci-monitor skill by asking your AI agent:
1Commit the work, create a PR and monitor CI.
2Prerequisite: Your Nx workspace needs to be connected to Nx Cloud and you should have Self-Healing CI enabled. More about that in the docs.
The skill connects to Nx Cloud, watches pipeline progress, and feeds failure information back to your agent. If Self-Healing CI proposes fixes, those become available for your agent to review and apply.
Want to try Ralph loops yourself? Check out this example repo for a working setup you can reference.
Looking Ahead
Software development is changing rapidly. AI coding agents are becoming part of everyday workflows, and the quality you get out of them vastly differs based on your setup.
The infrastructure matters. How your codebase is organized. The type and quality of context the AI agent has access to. Whether guardrails and feedback loops are in place. Whether your CI platform integrates deeply enough to close the autonomy gap.
These factors determine whether your AI agent can become a productivity multiplier.
Nx enables these autonomous flows. This is just the beginning.
Learn more:
- Autonomous AI Agents at Scale: Infrastructure requirements for AI agent workflows
- Self-Healing CI Documentation: How Nx Cloud's Self-Healing CI works
- Ralph Wiggum (original concept): Community origins of the Ralph loop pattern
- 🧠 Nx Docs
- 👩💻 Nx GitHub
- 💬 Nx Official Discord Server
- 📹 Nx Youtube Channel
