Axum is a Rust workable choice for edtech. GreatCTO auto-detects both — adds the edtech archetype overlay, wires edtech-specific gates, and runs 83 specialist agents around your existing Axum workflow.
GreatCTO reads your Cargo.toml and detects axum + edtech archetype from signals: imports, file structure, env vars, README hints.
Attaches the edtech archetype overlay: archetype-specific reviewer + compliance gates. Override if your specifics differ; the defaults are sensible for Axum-style projects.
qa-engineer runs cargo clippy / cargo test / cargo-tarpaulin; security-officer audits unsafe blocks + dependency tree; performance-engineer reviews allocator patterns.
Bugs you've hit before in other Axum 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-axum-app && npx great-cto init ✓ scanning manifests… found manifest ✓ stack: axum (Rust) ✓ archetype: edtech ⚠ archetype + stack combo is unusual — review overlay manually ✓ 83 agents ready $ /start "add course enrollment endpoint" ▸ architect drafting ARCH-edtech.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 "course enrollment endpoint" — auth first, schema validation, and the audit line the edtech reviewer requires before gate:ship opens.
// src/handlers/edtech.rs — drafted by senior-dev, reviewed by 5 agents
pub async fn create(
user: AuthenticatedUser, // security-officer: extractor rejects anon
State(state): State<AppState>,
Json(req): Json<EdtechRequest>, // qa-engineer: serde validation
) -> Result<Json<EdtechResponse>, AppError> {
let result = handle(req, &user, &state).await?;
state.audit.record(user.id, "course enrollment endpoint", result.confidence).await?; // gate:edtech
Ok(Json(result))
}edtech overlay.Student tools under COPPA / FERPA with parental consent.
Classroom platforms meeting Section 508 / WCAG 2.2 AA.
K-12 products aligned to state student-privacy laws.
Axum (Rust) is not a typical fit for edtech. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the edtech 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.