AI Agents
14 weeks · 9 projects
Agentic engineering school
Cohort-based programs with working mentors, in-browser labs, and real projects — from prompt to production.
Completion
0%
Avg. comp uplift
$0k
Time to offer
0 wk
Mentor ratio
1:0
For teams
Upskill your org on agents, ML, and MLOps — with a shared cohort rhythm.
14 weeks · 9 projects
16 weeks · 11 projects
12 weeks · 8 projects
01
Ship real systems every week — not slide decks.
02
Monaco + sandboxes. Run, test, iterate without local setup.
03
Working engineers review your work and unblock you fast.
04
Portfolio, interviews, and hiring partner intros baked in.
from neuron import Agent, tool
@tool
def search(q: str) -> str:
return f"results:{q}"
agent = Agent(tools=[search])
agent.run("Plan + execute a rollout")TRACE
01 plan · 120ms
02 tool search("rollout") · 340ms
03 summarize · 90ms
done · 2.1s
Staff ML · ex-Stripe
Platform eng · ex-Vercel
Applied AI · ex-OpenAI
Infra · ex-Databricks
“I went from CRUD APIs to shipping an agent that runs weekly ops — with code review that felt like a real team.”
Priya N.
Backend → AI engineer · Series B SaaS
“The lab loop forced rigor: evals, retries, tracing. That’s what interviews actually asked about.”
Marcus T.
Senior eng → AI lead · Fintech
“Mentor async + 1:1s kept me moving. The cohort feed made it feel less lonely than other online programs.”
Elena V.
CS grad → first AI role · Startup
No. Tracks start from strong programming fundamentals; we ramp you into agents and production patterns step by step.
Most students plan 10–14 hours weekly. Heavier weeks align with lab deadlines and mentor reviews.
In-browser editor, sandboxed runs, and the same observability patterns you’d use on a real team.
Career track includes structured outcomes support; guarantee details are outlined on the pricing page.
Take the placement quiz — we’ll match you to a track and cohort start date.