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I have shipped over thirty extensions for Swamp in the last two months. Models for AWS, Cloudflare, GitHub, Reddit, Redmine, PostgreSQL. Vaults, datastores, workflows, reports. All of it generated by an AI agent. And nearly all of it would have been structurally wrong without a system designed to catch structural mistakes before they ship. The agent writes fast. It writes plausibly. It writes code that passes type checks and lint on the first try. What it does not do, reliably, is reason about the system-level consequences of its local decisions. It will generate a paginator that works for 50 results and silently degrades at 50,000. It will reuse an instance name across two methods because the name looks right in isolation. It will swallow a rate-limit header because the happy path never triggers the branch. ...

The sandbox that teaches you swamp is itself managed by swamp. A system whose primitives are general enough to manage cloud infrastructure, editorial workflows, and security scans can also manage the disposable learning environment that introduces all of those patterns. The problem with demos Most developer education follows a pattern: here is a tool, here is a contrived example, here is a README explaining how the contrived example relates to your real work. The gap between the tutorial and production is where most people give up. ...

Every Terraform file you ever wrote was a note to a blind tool. You looked at your infrastructure, decided what it should be, wrote that decision into HCL, and handed it to a program that could not see for itself. The declaration was your observation, pre-computed and frozen into a file. The same was true for Chef recipes, Puppet manifests, and CFEngine promises. You observed the system. You wrote down what you saw and what you wanted. You gave that note to an agent that could parse files but could not look around. ...

Picture three agents running concurrently against the same repository. One is implementing a feature. One is scanning the dependency tree for known vulnerabilities. One is validating that the infrastructure change will not violate the compliance baseline. They share typed state through a common data layer. When the security agent finds a vulnerable transitive dependency, the implementation agent sees that result immediately via a CEL query and adjusts its import before the PR is even open. ...

By default, swamp stores everything in local SQLite. This works until you close your laptop, sit down at your desktop, and discover that your agent has no memory of what happened an hour ago. The models are there (they live in YAML), but the runtime data, the execution history, the versioned outputs from every method run, all of it is trapped on whichever machine produced it. If you work on one machine, this is fine. If you work on two or three, it is a problem. Your agent starts cold every time you switch. The voice profile you refined yesterday on your laptop does not exist on your desktop. The health check workflow that ran at 03:00 on your VM has results you cannot see from anywhere else. You are running the same models against the same code, but the data layer is fragmented across physical locations. ...

If you have used swamp.club at all, you probably think of it as an infrastructure automation tool. The ecosystem gravitates that way. The most-pulled extension is @john/k8s with 15 model types wrapping the Kubernetes API. The examples in the docs show pod health checks and deployment workflows. The leaderboard is full of people automating infrastructure tasks. That framing is incomplete. What swamp actually provides is a typed, versioned, schema-validated data layer for AI agents. The primitives are: models with Zod schemas, immutable versioned data, method execution, and composable workflow DAGs. Those primitives do not care whether the data flowing through them is pod status or prose rules. ...

A collision is coming that most teams have not noticed yet. On one side you have the workflow your team actually uses. If you run a platform or operations team, it probably looks something like Kanban: pull-based flow, WIP limits, design sessions before implementation, a small number of states that everyone understands. The workflow exists to serve the people. You have spent years tuning it. It works. On the other side you have the lifecycle your AI agent needs. If you are using an agentic framework — Swamp, or something like it — the agent operates through a state machine with enforced transitions, upfront planning, adversarial review gates, and checks that physically prevent skipping steps. The lifecycle exists to constrain the agent. It works. ...

I wrote recently about the difference between easy and simple.1 That post was philosophical. This one is not. This is what it looks like in practice. I have been building an AWS operations toolkit using Swamp. The toolkit investigates outages. It runs daily health checks. It composes nine separate extensions into two workflows that gather data from CloudWatch, X-Ray, EC2, Lambda, and load balancers, then produce an actionable report. This is the machine that builds the machine. Not the code. The system that produces the code and makes sense of the output. ...

We are living through a strange professional silence. Many of us use AI every day while feeling a quiet guilt about it. We worry that by offloading the labor of coding, we are offloading our value as engineers. We reach for the tool because it works. Then we wonder what it means that we needed it. The discomfort is not about the technology. It is the sense that we are moving faster than we can think. ...

Growing into SparkleFormation In my continued usage with SparkleFormation I’m really growing to appreciate the convenience of having ruby available when composing templates. Things that would be challenging in a simplistic serialization format or lead to unmanageable duplication become easily solvable thanks to the powerful combination of an actual programming language, which json and yml are not, and the simplicity of the SparkleFormation DSL. At it’s simplest you can mirror the structure of any cloudformation json (and finally have comments inline!) but you quickly discover more advanced use cases. ...