Infrastructure

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.” ...

I wanted to know if a swamp datastore could survive a swarm, so I asked an agent to design one, pointed it at ECS Fargate, and stepped away. A custom Docker image, a fleet of remote workers, and a simple execution plan: model method calls firing events as fast as the workers could generate them. The final cluster ran 517 tasks: 500 loop workers each pushing telemetry at roughly 1.22 events per second, a single orchestrator running swamp serve against 50 models on a shard-first S3 datastore, and 16 serve-dispatch workers standing by for workflow batches. At full tilt that’s north of 600 events a second, sustained. The point was lock contention: S3 versus my Postgres extension, under real concurrent write pressure, the kind a global team of hundreds would eventually produce whether I tested for it or not. ...

The drift-state extension makes zero AWS API calls. It reads data that other models already produced, diffs it against a stored baseline, and writes the result as typed versioned state. The “drift detector” is a composition function over existing observations. This matters because drift detection is usually sold as a feature of a specific tool. CloudFormation has drift detection. Terraform has plan. AWS Config has compliance evaluations. Each one instruments its own slice and reports on what it manages. Nothing composes across them. ...

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. ...