Spring Boot is a Java workable choice for infrastructure / iac. GreatCTO auto-detects both — adds the infra archetype overlay, wires infra-specific gates, and runs 83 specialist agents around your existing Spring Boot workflow.
GreatCTO reads your pom.xml / build.gradle and detects spring-boot + infra archetype from signals: imports, file structure, env vars, README hints.
Attaches the infra archetype overlay: archetype-specific reviewer + compliance gates. Override if your specifics differ; the defaults are sensible for Spring Boot-style projects.
qa-engineer runs ./gradlew check / SpotBugs / JaCoCo coverage; security-officer scans for deserialization sinks + Spring SpEL injection; performance-engineer profiles JVM + GC patterns.
Bugs you've hit before in other Spring Boot projects (connection-pool exhaustion, ORM N+1 queries, retry storms) — the agent's Step 0 includes the prior detection order. MTTR drops 94 % on second occurrence (methodology).
$ cd my-spring-boot-app && npx great-cto init ✓ scanning manifests… found manifest ✓ stack: spring-boot (Java) ✓ archetype: infra ⚠ archetype + stack combo is unusual — review overlay manually ✓ 83 agents ready $ /start "add infra feature" ▸ architect drafting ARCH-infra.md… ▸ pm decomposing into beads tasks… ⚐ gate:plan — your approval needed
Approve → 3 senior-devs run in parallel worktrees → 5 reviewers fan out in parallel → gate:ship → deploy. One real run walked stage-by-stage: /proof.
This is the shape of what senior-dev drafts for "infra feature" — auth first, schema validation, and the audit line the infra reviewer requires before gate:ship opens.
// src/main/java/.../InfraController.java — reviewed by 5 agents
@RestController
@RequestMapping("/infra")
class InfraController {
@PostMapping
@PreAuthorize("isAuthenticated()") // security-officer: auth before handler
ResponseEntity<Result> create(@Valid @RequestBody InfraRequest req,
Principal user) {
var result = service.handle(req, user); // qa-engineer: bean validation enforced
audit.log(user.getName(), "infra feature", result.confidence()); // gate:infra
return ResponseEntity.ok(result);
}
}infra overlay.Terraform / Pulumi stacks with drift detection.
IAM policies reviewed for least privilege.
Helm / CDK deploys with rollback paths enforced.
Spring Boot (Java) is not a typical fit for infrastructure / iac. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the infra archetype page for the typical stack list and decide if your case is the right tool / right archetype.
No black-box "AI does it all" loop. GreatCTO is a deterministic state machine — 8 stages, 22 nodes, 2 human gates. Every node maps to a real agent on GitHub. Inspect the state machine →
$ npx great-cto init
Free, MIT, runs locally. Built as a Claude Code plugin — install with one command.
Eight stages, two human gates, four memory layers. Why this exact shape, and what I tried that didn't work.
One run, one feature, from prompt to merged PR. Time, cost, and gate-by-gate breakdown — no marketing math.
Regex vs LLM-based archetype detection, the false-positive count, and why I keep rejecting the obvious fix.
The bottleneck in agentic SDLC isn't model quality — it's process governance. Here's the state machine that closes the gap.