New training program

Vibe Coding & Agentic Programming

From zero to 100: from casual AI usage to building agents and products 3x-5x faster

This program was designed for technical teams that want to stop using AI in an improvised way and start incorporating it as a real working capability. In 4 weeks, the team learns from the practical foundations of LLMs to building and orchestrating agents, including discovery, context, tools, planning, memory, multi-agent systems, and internal adoption.

Powered by Stock42 - 2 years building agents and more than 50 projects created with vibe coding / agentic programming.

This is not an isolated prompt engineering course.

This is not a superficial workshop.

It is applied training so the team can use AI as a production system.

I want to bring this program to my team View full program

Objective

Program objective

We want the team to move from asking a chat for things to:

defining a problem clearly

structuring context

using specialized tools

building useful agents

orchestrating sub-agents

bringing that capability into the real development workflow

Real problem

Your team already uses AI. The problem is how it uses it.

Many technical teams already use ChatGPT, Claude, Cursor, or v0. But they do it in isolation, improvising, and without a shared methodology.

inconsistent results

dependence on the few people who figured it out

lots of experimentation, little real change in how the team produces

This program fixes that.

What the team learns

From asking better, to building better

This is not a prompts course. It is applied training so your team learns to:

understand how LLMs actually work

do discovery and documentation with AI

use coding tools with professional judgment

design context and skills

build agents with tools

incorporate memory, planning, and ReAct

take first steps in multi-agent systems

bring all of that into the real development workflow

Outcomes

What your team takes away in 4 weeks

a clear mental model of how generative AI works when applied to development

judgment for choosing tools based on the task type

a professional vibe coding methodology

a starter kit with reusable context files

a functional agent built during the course

an evolved version of that agent with memory, planning, and tools

a practical introduction to multi-agent systems

guidelines to adopt this way of working inside the company

Detailed program

Program

Week 1

Foundations + Discovery

Session 1

Generative AI and LLMs for developers

Build a shared foundation so the entire team understands what it is using and how to use it better.

What an LLM is and what it is not

Tokens, context, and real limits

Pretraining, instruction tuning, and reasoning without hype

Why an LLM does not think like a human

Typical errors: hallucination, context loss, and overconfidence

Differences between models and when choosing well matters

Session 2

Chat interfaces, discovery, and professional documentation

Turn chats into discovery and design instruments, not just improvised assistants.

ChatGPT, Grok, Claude, and practical differences in usage

Projects, artifacts, canvas, uploads, memory, and organization

How to use chat for feature and product discovery

Generating better specifications

Defining scope, constraints, and stack from the start

Turning conversations into useful build-ready documents

Week 2

Professional vibe coding + context management

Session 3

Professional vibe coding and specialized tools

Learn to write vibes that produce useful work and not just pretty text.

What vibe coding is and why it is not just prompting

The difference between asking, guiding, iterating, and directing

v0, Claude Code, Codex, Cursor, and when to use each one

Tasks where AI truly accelerates

Tasks where strong human control still matters

Patterns for generating UI, logic, scripts, and prototypes with less friction

Session 4

Context engineering, skills, and project setup

Teach the team how to create the right context so agents can work well.

Agent skills

Base files: AGENTS.md, PROJECT.md, TODO.md

Designing persistent instructions

Managing long context

Operational memory vs working context

How to start an agentic project professionally

Week 3

Agent building

Session 5

Building a simple agent with tools

Move from theory to a real functional agent.

Architecture of a simple agent

What tools are and why they change everything

Basic execution loop

Connecting to the model

Internal and external tools

Basic agent testing

Guided construction of the first agent

Session 6

Evolving the agent: memory, planning, and ReAct

Take the agent to a more useful, robust, and autonomous version.

Short-term vs long-term memory

Task planning

ReAct in practice

Self-reflection and self-correction

How to improve vibes and context for long-running agents

Debugging and basic behavior evaluation

Week 4

Multi-agent + real adoption

Session 7

Multi-agent systems and orchestration

Introduce a more powerful architecture without making the course excessively complex.

When one agent makes sense and when several do

Supervisor + planner + coder + tester

Feedback loops

Automatic return on errors

Basic orchestration design

Limits, risks, and good practices

Session 8

Final project, team adoption, and ROI

Close the course by turning what was learned into a concrete working capability.

Final project presentation

How to incorporate AI and agents into the real team workflow

Discovery, code review, feature factory, QA, and internal automation

Operational risks, minimum governance, and human control

How to measure real impact

Adoption plan for the next 4 to 8 weeks

Final project

During the program, participants build a guided project that integrates:

strong project context

skill definition

use of AI coding tools

a functional agent with tools

an evolved version of the agent with planning or memory

a simple orchestration or collaboration scheme between agents

The goal is not only to see concepts, but to finish with something that can serve as a real base.

Methodology

The methodology combines:

live class

demonstration

guided building

applied exercises

short homework

progressive project

The logic is simple: understand -> design -> build -> evaluate -> adopt

Benefits for the company

accelerates discovery and feature definition

reduces friction for prototypes and first implementations

improves real AI usage inside the team

creates a common language across devs, leads, and product

reduces messy experimentation

installs a reusable base for agentic work

Benefits for the team

they truly understand what LLMs do and do not do

they learn to write better vibes

they gain better judgment about tools

they build real agents

they learn to work with context, memory, and tools

they leave with a much more professional methodology

Requirements

Recommended requirements

prior software development experience

basic understanding of software architecture and APIs

comfort with repositories, files, and development tools

willingness to experiment and build during the course

Why Stock42

Stock42 does not teach this from theory or hype. It teaches it from real practice.

2 years building agents and agentic programming systems

more than 50 projects developed with vibe coding

real experience in architecture, products, automation, and technical adoption

a vision oriented to startups, SMBs, and teams that need results, not fashion

Investment

Up to 5 participants: US$ 2,490

Up to 10 participants: US$ 3,990

Up to 20 participants: US$ 5,990

Additional participant: US$ 290

Ideal for startups, SMBs, and technical teams that want to adopt AI with speed, but also with judgment.

Request proposal

FAQ

Do we need prior experience with agents?

No. The program is designed to take the team from strong fundamentals to applied building.

Is it a technical or strategic course?

It is both, but with a practical emphasis. It is designed so the team understands and builds.

Is it useful for seniors who still do not master LLMs?

Yes. In fact, that is one of the profiles that benefits the most: people with deep technical experience who learn fast once they understand the mental model.

Is the course generic or adapted to the team?

It is delivered with an in-company focus, so it can be contextualized based on the product type, stack, and maturity of the team.

What happens after the course?

The team leaves with conceptual foundations, applied practice, a final project, and a much clearer way to continue incorporating AI and agents into daily work.

Your team does not need to use more AI. It needs to use it better.

This program is designed for teams that do not want to sit back and watch software development change, but to participate actively in that transformation. The goal is not to replace developers. The goal is to multiply their capability.

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