Ship one agent before you teach the next
Every pattern in the curriculum has been put into a real business first. If it isn't running, it isn't a lesson yet.
About A8gent
The production playbook for AI agents, built by someone who actually ships them.
Most AI agent courses online are written by people who read the docs once. A8gent is written by an engineer who has had agents wake him up at 2am - and figured out how to keep that from happening twice.
Taught by
Deepak
ML Architect & Full Stack Engineer
Machine Learning Architect and Full Stack Engineer building practical AI agent workflows for business teams. 10+ years shipping production ML across TensorFlow, PyTorch, AWS, and GCP. Active open-source AI contributor — and the person who ships every A8gent agent before it becomes a lesson.
10+ yrs production ML
TensorFlow · PyTorch · AWS · GCP
Open-source AI contributor
Ships agents weekly
Why A8gent exists
The gap between AI hype and AI that ships is growing.
There are thousands of tutorials on building AI agents. There are very few on building agents that survive production - that handle the weird inputs, the rate limits, the model drift, the angry customer who got a wrong refund.
I've been shipping ML systems into production since 2014. When LLMs started showing up in real products in 2023, I noticed something familiar: the same gap between "works in a notebook" and "works on a Monday morning when there are 10,000 users" was opening up again, just with new tools.
The tutorials online taught the playground version. Get Claude to call a tool. Wire up a RAG demo. Spin up an MCP server that says hello. Useful in week one. Useless when the same agent is supposed to handle real money, real customers, and real ambiguity in week ten.
A8gent is the production playbook I wish existed when I started: which workflows are worth automating, how to design the guardrails before the loop, how to evaluate the agent honestly, how to roll it out to a team without a revolt. The patterns work because they're already running in real businesses.
Four principles
What every course in the library is built around.
These principles are what separate an A8gent lesson from a YouTube tutorial. They're applied to every module.
Guardrails before glamour
Approval levels, eval harnesses, escalation rules. The boring stuff that decides whether the agent survives week 3 in production.
Workflow first, tools second
Pick the work worth doing, then the tool that fits. n8n, Make, Vapi, code - all valid. None is the answer alone.
Outcomes over outputs
A lesson that doesn't end in a shipped agent is entertainment, not training. We end each module with something that runs.
The road here
Why this curriculum can only have been written now.
Some context for why the patterns in A8gent are the ones they are.
2014-2018
Production ML from the trenches
Shipped ML systems across recommendation, search, fraud, and NLP at scale on AWS and GCP. Learned that 'works in the notebook' and 'works on Monday morning' are different problems.
2019-2023
Full-stack engineering + the open-source years
Bridged ML and product engineering. Contributed to open-source AI tooling. Watched LLMs go from 'cool demo' to 'every team wants this in production'.
2024-2025
Agents move from playground to production
Built and shipped agents into real businesses - support, sales, internal ops. Learned which patterns survive and which break. Most agent courses online taught the playground version.
2026
A8gent
The production playbook, packaged. Courses, templates, MCP servers, and a community for engineers and operators shipping real agents.
What's in the system
The whole library, lifetime access.
One-time payment. No subscription games. 7-day money-back guarantee. Lifetime updates as the tools change.
10 self-paced courses, video + text
All templates, n8n / Vapi configs, MCP servers
Private community for paid students
Monthly live workshop, recorded for the library
Every course we add in 2026 / 2027 if you're on All-Access
Priority email support on All-Access
Next step
Don't let the AI agent skill gap widen.
Start with one useful workflow, prove the ROI, then roll repeatable agent patterns out to your team — before competitors make it normal.