AgentDefinition
The recipe: prompt template, model, allowed tools, permissions, and output sinks.
AI agent subsystem
Run autonomous coding agents as first-class Kubernetes resources. Describe the work, pick a runtime, launch a run. The controller does the rest.
What is the ai agent subsystem?
Section titled “What is the ai agent subsystem?”ai-agent-subsystem turns “run a coding agent” into a declarative Kubernetes operation. You
describe what the agent should do (a recipe), where it runs (a runtime template), and which
run you want (parameters). A controller reconciles each run into a Kubernetes Job, supervises it,
streams its output, and records the result on the resource’s status. Kubernetes stays the single
source of truth.
It is a clean-sheet, standalone rebuild of an internal subsystem, written in D as a statically linked monorepo with no runtime dependencies.
The three resources
Section titled “The three resources”Everything is built from three Custom Resources that reference each other in a chain:
flowchart LR
AD["AgentDefinition<br/>the recipe"] -->|agentDefRef| ST["Station<br/>the runtime"]
ST -->|stationRef| AG["Agent<br/>one run"]
AG -->|controller creates| JB["Job → Pod"]
classDef ad fill:#2f5fd8,color:#fff,stroke:#16224f;
classDef st fill:#3f7be0,color:#fff,stroke:#16224f;
classDef ag fill:#5b93ec,color:#fff,stroke:#16224f;
class AD ad
class ST st
class AG ag
Station
The runtime: a Pod template plus a recipe reference and run-history limits.
Agent
One run: a Station reference, parameters, and a status that tracks the lifecycle.
Controller
Watches Agents, builds Jobs, patches status, and prunes old runs.
Start here
Section titled “Start here” How the pieces relate The full relationship schema: data model and runtime composition diagrams.
Architecture The D monorepo: three binaries and a shared library, statically linked.
Install One kubectl apply of a signed release manifest stands up the whole subsystem.
Examples Worked recipes: a bug-fixer and a pure-text story writer.