Prototype fast. Build for real. Actually charge for it.
8 weeks. 48 hours live. Take one idea from a no-code prototype to a deployed, evaluated, monetisable agent product. Learn the craft that gets agents past the happy path and into paying customers' hands.
✓ Live sessions Saturdays & Sundays · 8:00 PM – 11:00 PM WAT · Cohort 1 · Now Open
The demo goes viral. Three months later there is no product. The gap is not model quality — it is everything around the model.
The happy path looks magical. Then users do something you did not anticipate and the agent confidently ruins their day. Without evals, guardrails, and recovery, the demo stays a demo.
No-code tool this week, Python framework next week, graph-based framework the week after. Every rewrite burns momentum. You need a path that starts fast and survives the switch to production code.
Most agent tutorials stop at agent.py. Real products need a frontend users trust, auth, billing, usage limits, and observability. Without those, nobody pays you for the agent — no matter how clever it is.
Start by proving the idea fast. End by wrapping, deploying, and productising the one that worked.
From idea to a Python agent with tools
Learn the design principles that separate agents that work from agents that break. Ship a no-code prototype in Week 2. Port it to a production-grade Pydantic AI agent with tools and a real RAG pipeline by Week 4.
From agent to deployed, paying product
Wrap your agent in a real app — backend, frontend, auth. Containerise and deploy. Layer in advanced architectures and an eval harness. Finish with a capstone SaaS complete with billing and usage limits.
Each week ends with something deployed, tested, or paid for. The pieces compound into a real product.
Agents are loops with tools, not chatbots. Learn where they win, where they break, and how to scope one so it actually ships. Leave Week 1 with a tight design doc for the product you will build across the cohort.
Prototype the agent end-to-end in a single weekend using n8n. Wire up a model, a few tools, a trigger, and a lightweight UI. The goal: a shareable link you can actually run past friendly users before writing a line of Python.
Port the prototype to a real Python agent using Pydantic AI. Typed inputs, typed outputs, typed tools. Add system prompts, retries, structured results, and a small test suite that keeps the agent honest.
Give the agent memory and documents. Build a production RAG pipeline — chunking, embeddings, hybrid search, re-ranking — and expose it as a clean tool the agent can call. Add a harness to check retrieval quality.
Wrap the agent in an app users can actually use. Build a FastAPI backend around the agent, a React frontend with streaming, and auth plus session storage on Supabase. The agent stops being a script and becomes a product.
Containerise the stack. Deploy with Docker and a reverse proxy. Add tracing, cost monitoring, and alerting so you know when the agent misbehaves at 3 AM. Learn what to log, what to sample, and what to ignore.
Go beyond the single agent. Add a supervisor routing requests across specialists. Add a human-in-the-loop checkpoint for risky actions. Wrap the whole thing in an eval harness so every change is measured, not guessed.
Turn the product into a business. Add billing, plans, usage limits, and a landing page. Pick a pricing model that matches your cost profile. Present a capstone SaaS you could actually charge for — with the numbers to back it up.
Pragmatic, production-leaning choices. Nothing exotic. Everything replaceable.
OpenAI, Anthropic, Google, open-source via OpenRouter
n8n nodes, webhooks, HTTP tools, shareable demos
Pydantic AI, LangGraph, tool schemas, typed results
PGVector, Supabase, Pinecone, hybrid search, re-rankers
FastAPI, async Python, WebSockets, Server-Sent Events
React, Vite, TanStack Query, streaming chat UIs
Supabase auth, Postgres, row-level security, session storage
Docker, Docker Compose, Caddy, VPS or managed cloud
Stripe, usage metering, cost tracing, Langfuse-style observability
One price, full stack. Curriculum, code, reviews, and a capstone you own.
Notes from builders previewing the curriculum ahead of the live cohort.
"I had been rebuilding the same agent in three frameworks for a year. The prototype-to-production playbook stopped the rewrite loop. I shipped a real agent to three customers in the first month."
"The eval harness was the missing piece. I used to ship a prompt change and pray. Now every change is scored before it leaves my laptop. The product got boringly reliable, which is what users actually want."
"I am a designer. I left the bootcamp with an agent, a frontend, a deployed backend, and a working Stripe checkout. That combination used to feel unreachable without a technical co-founder."
"Week 5 was worth the fee alone. Wrapping the agent in FastAPI and React took it from a toy in my terminal to something I could show my team. The streaming UI got applause in the standup."
"Starting from n8n was a cheat code. I proved two ideas did not work before I wrote any Python. The third one became the capstone and is now my side business."
"The supervisor / specialist pattern week changed the product. What was one over-stretched agent became three focused ones handing off cleanly. Error rates dropped sharply."
AI Engineer & Founder, SoftBricks
Builds and runs production agent products for clients across Africa and Europe. Architect behind StudyMate AI, an agentic platform in production for onboarding, moderation, meetings, and support automation — prototyped in n8n, ported to Python, wrapped in a real app, deployed, monitored, and monetised. The curriculum is the exact path walked on every shipped product.
This is a builder's bootcamp. The people who get the most out of it turn up with an idea they want to ship.
Everything needed to walk out with a deployed, billable agent product.
✓ Secure checkout · EMI available at checkout
Enrol today, get immediate access to the recordings, and have a working no-code prototype of your agent before the live cohort begins.
Enroll Now — $499The questions most builders ask before enrolling.
Reading Python comfortably is enough to start. You will write more Python as the weeks progress, but the curriculum teaches what is needed in context. No prior experience with agent frameworks is expected.
Plan for 10–15 hours a week — 6 hours live across Saturday and Sunday, plus 4–9 hours for the weekly build. Shipping an agent product in 8 weeks is real work.
Helpful but not required. Week 1 includes a scoping exercise that takes rough ideas and sharpens them into something shippable. A seed brief is provided for anyone arriving blank.
Agentic AI goes deep on agent engineering with LangGraph as the primary framework — designed for engineers building agent infrastructure. AI Agent Mastery is product-first: prototype, build, wrap, deploy, monetise. Most builders take Mastery first; the engineering-depth crowd takes Agentic AI next.
It needs to be deployable, testable, and billable — not necessarily live with paying customers on graduation day. Most graduates leave with a product that is one marketing push away from first revenue.
Every session is recorded and posted within 24 hours. Unlimited cohort re-attendance is included, so any week you missed you catch live next time — at no extra cost.
Yes. EMI is offered at checkout. For any other arrangement, reach us at academy@softbricks.ai.
Completely. Your agent, your code, your customer list if you get one. The academy takes no equity and no cut of anything you build.