Swamp launches officially today. The team asked whether I’d write something for the launch, and I said yes on the condition that it be what I actually think. I’m a user, a community contributor, and a paying customer. Best effort I was trying to get an agent to work with the infrastructure tools I already had. CloudFormation ran some stacks and Terraform ran others. I wanted an agent in the middle to plan changes, apply them, and tell me what happened. ...
Short enough to remember, precise enough to disagree with. 1. Observation is the primary operation Action follows observation; it never precedes it. A system that acts without first observing is blind, regardless of how sophisticated its declaration layer is. 2. Scaffolding is removable. Principles are not Autonomy, locality of knowledge, voluntary commitment, evidence over authority. These survive every tool generation. Sprint ceremonies, state files, approval queues, and required reviewers are scaffolding. The work is knowing which is which. ...
Mark Burgess shipped CFEngine in 1993, formalized promise theory with Siri Fagernes around 2005, and published In Search of Certainty in 2015. The model is precise: autonomous agents observe their environment, reason locally about what they find, and make promises about their own behavior. The agent does not receive instructions from a controller. The agent looks around, decides what to do, and commits to doing it. Every infrastructure tool since has claimed some lineage from this idea. Chef called its resources “convergent.” Puppet called its catalogs “desired state.” Terraform called its plans “declarative.” Ansible called its playbooks “idempotent.” ...
Hand someone a typed model in swamp and ask them to define what data they want to capture. Watch what happens. The schema requires them to name the fields, specify the types, declare what constitutes a valid observation. Most people stall. Not because the tooling is hard. Because they have never been asked to articulate what they actually want to know with that level of precision. The same pattern repeats at every layer. Ask them to write a workflow DAG and they cannot specify which steps depend on which, because they have never separated their assumptions about sequence from actual causal dependencies. A CEL query against versioned data exposes the same gap from a different angle: you must know the question before you can ask it, and most people have never distinguished “what do I want to know” from “what does the system happen to show me.” ...
The declaration authorized the reconciler and the ceremony authorized the team, and neither required a trust decision because the scaffolding decided for them. The declaration told the blind tool what reality should look like. The ceremony told the blind organization what the work should look like. Both existed because the system could not observe for itself, so we built structures that made observation unnecessary by embedding the answer in advance. ...
You have agents running in your system. Your first instinct was to add controls. Approval gates before they execute. Escalation policies when they’re uncertain. Manifests declaring what they’re allowed to do. Kill switches for when they go wrong. This is the same instinct that produced the approval queue for humans. You are governing agents the way you governed people, because that is the only governance you know. Deming had a name for the knowledge required to manage a system well. He called it the System of Profound Knowledge.1 Four components, never meant as a checklist: what is the system, where does it vary, how do you know what you know, and why do people behave the way they do. ...
Someone approved your last deploy. Maybe it was a thumbs-up on a pull request. Maybe it was a green check from a required reviewer who opened the diff, scrolled for eight seconds, and clicked “Approve.” Maybe it was your own merge after CI passed. Regardless of the ceremony’s size, the question is the same: what did that approval actually verify? Not what it was supposed to verify. What it did verify. What evidence exists that a specific risk was evaluated, a specific judgment was exercised, a specific context was held that the system could not hold on its own. ...
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. ...