Training course · Agentic Coding · MVP Development for Startups

Agentic Coding for Startups and MVP Development

Use autonomous coding agents effectively: specs instead of prompts, task decomposition, test-driven loops, multi-agent setups and governance.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Agentic Coding for Startups and MVP Development” is a hands-on course for roles such as Founders with little or no programming experience, Technical co-founders and first engineers in startups and Product managers in early-stage product teams. Rather than learning Agentic Coding in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from MVP Development for Startups.

Agentic coding means working with AI agents that carry out development tasks largely on their own: they read code, plan changes, run commands and tests, and correct themselves based on the results. Tools such as Claude Code, OpenAI Codex, the GitHub Copilot coding agent and Cursor’s agent mode have made this way of working practical for everyday use. This vendor-neutral course teaches the underlying methodology so that you achieve reliable results whatever the tool, and redefine the developer’s role within your engineering team.

The core shift is from ad hoc prompts to robust specifications. You will learn to decompose and describe tasks so that agents solve them reliably, to set up test-driven loops in which tests act as objective acceptance criteria, and to run several agents in parallel or with a division of labour. We also cover review strategies for agent-generated code and team governance: permissions, security boundaries, accountability, cost control and the gradual introduction of agentic workflows into your existing development processes.

For founders and early product teams, speed determines how fast they learn from the market. AI coding tools, vibe coding and app builders now make it possible to turn an idea into a working minimum viable product with sign-up, database, payments and a landing page within days, often without a large engineering team. The real challenge shifts as a result: which hypothesis are you testing, which features truly belong in the MVP, and how does a quick prototype become a codebase that withstands investor due diligence, the first paying customers and growth? In this course you learn to build MVPs purposefully with AI agents, manage technical debt deliberately, build in security and data protection from day one and prepare the handover to a professional engineering team.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Founders with little or no programming experience
  • Technical co-founders and first engineers in startups
  • Product managers in early-stage product teams
  • Corporate innovation and venture building teams
  • Participants in incubator and accelerator programmes

Typical challenges we address

  • Limited time and budget to validate a product idea in the market
  • Lack of engineering capacity or dependence on external agencies
  • Quickly built prototypes with security holes, open databases and unclear architecture
  • Uncertainty about when to rewrite an MVP and when to evolve it

What you will be able to do

  • You understand how coding agents work and choose the right tool and level of autonomy.
  • You write specifications and project context that agents can work with reliably.
  • You break initiatives into verifiable sub-tasks and steer agents through test-driven loops.
  • You use multi-agent setups and parallel work in a controlled way.
  • Turn a business idea into a focused MVP scope and build it yourself with AI tools.
  • Integrate sign-up, database, payments and analytics and check your MVP for common security gaps before launch.
  • Manage technical debt deliberately and prepare the codebase for growth, due diligence and an engineering team.

Course outline

Day 1

Module 1 Foundations of agentic development
  • How coding agents work: tool calls, context windows and feedback loops
  • Comparing tools: terminal agents, IDE agents and cloud agents
  • Recognising suitable tasks and choosing levels of autonomy deliberately
  • The new role in the team: from implementation to steering and acceptance
Module 2 Specifications instead of prompts
  • Describing tasks with goal, context, constraints and acceptance criteria
  • Keeping project context in files such as CLAUDE.md, AGENTS.md or rules
  • Plan-first approach: let the agent draft a solution and review it before implementation
  • Versioning specifications and maintaining them as part of the repository
Module 3 Task decomposition and test-driven agent loops
  • Breaking large initiatives into independent, verifiable sub-tasks
  • Having tests written first and using them as an objective stopping criterion
  • Using linters, type checks and builds as automatic feedback
  • Recognising stuck loops and restarting with a new strategy
Module 4 Multi-agent setups and parallel work
  • Defining specialised subagents for research, implementation, testing and review
  • Organising parallel work with branches, worktrees and isolated environments
  • Orchestration: merging results and resolving conflicts
  • Sharing tools and data sources through MCP

Day 2

Module 5 Review, security and team governance
  • Review strategies for large, agent-generated changes
  • Defining permissions, sandboxing and protection against prompt injection
  • Embedding agents in CI pipelines and pull request processes
  • Planning team guidelines, accountability, costs and a phased rollout
Module 6 Practice lab: MVP Development for Startups
  • From pitch to specification
  • Building the MVP by prompt
  • Payments and integrations
  • Landing pages and experiments
  • Pre-launch security check
  • Making the codebase hand-over ready
Module 7 Data protection, quality & transfer to daily work
  • Even an MVP processes real customer data: you learn to implement GDPR essentials such as privacy notices, data processing agreements and consent, manage API keys and secrets securely and clarify licensing and copyright questions around generated code that become relevant in due diligence.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

From pitch to specification

Translate the business idea, target group and core hypothesis into user stories, a data model and a prioritised scope that stays deliberately small and includes measurable learning goals for the market test.

Building the MVP by prompt

Generate a web app with sign-up, database, core feature and admin area step by step using AI agents or app builders, working in small, tested iterations with the code under version control.

Payments and integrations

Integrate subscriptions and one-off payments through a payment provider, transactional email, analytics and first integrations, with webhooks, error handling and securely stored API keys. Test and live modes stay separate.

Landing pages and experiments

Create landing pages, waitlists and pricing pages in several variants, track conversion events and run quick A/B tests to check demand and willingness to pay before investing in heavier development.

Pre-launch security check

Have the MVP code checked for open database rules, missing access controls, exposed keys and unprotected endpoints, and work through a short checklist for a safe go-live.

Making the codebase hand-over ready

Document the architecture, add tests, refactor critical areas and produce a technical overview that new engineers or a technical due diligence can follow quickly. This also helps investor conversations.

Possible hands-on projects

Waitlist landing page

A landing page with value proposition, email capture, double opt-in and a simple dashboard that analyses sign-ups and traffic sources.

Booking MVP

A small web app where customers book and pay for appointments, providers manage availability and both sides receive email confirmations.

Interview synthesis tool

A tool that collects notes from customer interviews, clusters statements by problems and wishes and shows evidence for or against the core hypothesis.

Prerequisites

You have several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. You have a concrete product idea or work in an early-stage product team; no programming skills are required, though a basic technical understanding helps.

Technical requirements

Your own laptop with Git, a terminal and your development environment, plus access to at least one coding agent such as Claude Code, Codex, Copilot or Cursor.

Data protection & compliance

Even an MVP processes real customer data: you learn to implement GDPR essentials such as privacy notices, data processing agreements and consent, manage API keys and secrets securely and clarify licensing and copyright questions around generated code that become relevant in due diligence.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Salzburg Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Bremen Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Online Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Augsburg Request

Frequently asked questions

How does agentic coding differ from vibe coding?

Vibe coding relies on fast, intuitive iteration in natural language and is particularly suited to prototypes. Agentic coding is an engineering discipline for professional codebases: precise specifications, tests as acceptance criteria, clear permissions and structured reviews ensure that agents deliver reliable, maintainable results even in complex, long-lived projects.

Is the course tied to a specific tool?

No. The course teaches methods that work with Claude Code, OpenAI Codex, the GitHub Copilot coding agent, Cursor and comparable tools. The exercises highlight differences and common ground, for example in context files, permissions and subagents. You can work with whichever tool your organisation already uses or plans to adopt.

How do we stay in control of quality and security with autonomous agents?

Control comes from several layers: tightly scoped permissions and sandboxing, tests and static analysis as automatic checks, mandatory human review before merging, and clear team rules on accountability. During the course you develop a governance concept that fits your processes, security requirements and regulatory obligations.

Can a non-technical founder really build an MVP alone?

For many business models, yes: web apps with sign-up, data storage, a core feature and payments can be built with AI tools without programming skills. What matters are clear requirements, small steps and systematic testing. Complex logic, high security demands or rapid growth will eventually call for engineering expertise. In the course you learn to recognise that point.

Will an AI-built MVP have to be rewritten later?

Not necessarily. If you use version control from the start, keep the data model understandable, test core features and separate configuration from code, an MVP can often evolve step by step. We show which early decisions make later rework easier and how to tell when starting afresh is the cheaper option.

How much does “Agentic Coding for Startups and MVP Development” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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