Today we’re introducing Pulumi Neo code reviews, now in public preview. Neo code reviews analyze pull request changes in conjunction with what Pulumi Cloud knows about your running infrastructure, providing both high-level and code-level feedback.
For about two years, the unit of work with a coding agent was the prompt. You wrote a good one, you gave it enough context, you read what came back, and you wrote the next one. The agent was a tool, and you were holding it the entire time, one turn after another.
That part is ending. Addy Osmani, a director of AI at Google Cloud, has a name for what replaces it, and I have not stopped thinking about it since: loop engineering. You stop being the person who prompts the agent. You design the loop that prompts it for you.
In my phrasing: you stop being the thing that runs, and start designing the thing that runs. The leverage moves up a layer. What I want to do here is take an honest look at the pieces, and at the part nobody automates.
AI coding has two shapes right now. One agent in a loop, sequential work, you babysitting the chat window. Call that 2x. Most teams live here. Five agents in worktrees, parallel work, fresh-context review on every change. Call that 10x. The trick: 2x is mostly prompting, 10x is mostly plumbing.
The parallel coding playbook is a five-pattern setup for running multiple AI coding agents at the same time without them stepping on each other: an issue used as the spec, a plan/build/validate loop, parallel git worktrees, fresh-session review, and a self-healing layer. The whole thing targets application code. The interesting question, and the one I keep ending up at, is what changes when the five agents are touching infrastructure.
Anthropic shipped a piece earlier this month called How Claude Code Works in Large Codebases. I have not read anything more useful about coding agents this year. The core claim, in their words: “the ecosystem built around the model—the harness—determines how Claude Code performs more than the model alone.” In my phrasing: in a real codebase, the model is the smaller variable. The layer of context and tooling you wire around the agent matters more than which version of Sonnet or Opus is behind it.
The post stays high-level, which is the right move for a launch piece. What I want to do here is land it. Same seven pieces, but with the wiring you would actually put in a repo, in the order I would put it.
Infrastructure as code is the right model for production systems. State tracking, drift detection, and repeatable deployments all matter when you’re managing real workloads.
But sometimes, you also need a quick, one-off interaction with the cloud: create a bucket or a database, look up a VPC, delete a stray resource.
Today we’re introducing pulumi do, a new command for direct resource operations. With pulumi do, you can create, read, update, delete, and query any cloud resource from the terminal with a single command, across thousands of Pulumi-supported providers — no project, code, or state required.
This week, Pulumi Neo started working in two more places: GitHub and Slack. The agent that already runs Pulumi tasks from the Cloud console and the terminal now participates in the threads where your team discusses changes.
Mention @pulumi-neo in a pull request or issue and Neo replies in the thread. Mention @Neo in a Slack channel and Neo starts a task, continuing the conversation as you reply.
Recurring platform work slips: provider versions fall behind, drift accumulates between checks, and the quarterly audit keeps getting pushed back another month. Pulumi Neo can now run any task on a cadence you set, opening a pull request for each run.
Ewan Dawson is CTO of Compostable AI, where five engineers run an AI-native software factory: nineteen clients, custom AWS deployments, most of them shipped within a day of contract signing. This article is adapted from his recent Pulumi webinar, and covers rules in more depth than we had time for on stage.
Since launching Pulumi Neo, over 4,500 organizations have used it to delegate real infrastructure work: scaffolding, migrating, investigating, operationalizing, and more. Though that usage has come entirely through Pulumi Cloud, we know a large portion of Pulumi users live in the terminal, and increasingly that’s where AI tools run too. Now we’re bringing Neo there.
pulumi neo brings the same Neo experience you’ve had in Pulumi Cloud to your terminal. Running locally means there’s no separate branch to push, no credentials to provision, and no context to paste: Neo picks up the setup you already have.
Pulumi Neo already understands your infrastructure: your code, your stacks, your state. Today we’re launching new capabilities that extend Neo’s reach in two directions: into the third-party systems your team uses to plan and observe, and out to the cloud CLIs that actually drive your infrastructure.
The first half is MCP integrations: connections to Atlassian, Datadog, Honeycomb, Linear, PagerDuty, and Supabase that show up as tools Neo can call during a task. The second half is CLI integrations: scopable access to aws, gcloud, az, and kubectl. Both are configured once at the org level and available to every Neo task in the organization.
