Training course · AI Agent Development · MVP Development for Startups

AI Agent Development for Startups and MVP Development

Build AI agents professionally: architectures, tool use, agent SDKs, LangGraph, memory, evals, guardrails and deployment.

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

This course connects AI Agent Development with the everyday processes of MVP Development for Startups. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

AI agents go beyond conventional chatbots: they break tasks down, call tools and APIs, evaluate the results and keep iterating until a goal is met. In this two-day course you learn to build such systems so that they work reliably, transparently and safely in day-to-day business. You compare architectural patterns ranging from simple workflows through the agent loop to multi-agent systems, and you implement them hands-on with the Claude Agent SDK, the OpenAI Agents SDK and LangGraph.

The focus is not on a quick demo but on the path to production. You design tool interfaces and context deliberately, connect tools via the Model Context Protocol, plan short-term and long-term memory and build evaluations that surface regressions early. On top of that come guardrails against prompt injection and harmful actions, human-in-the-loop approvals, tracing, cost control and deployment. By the end you can judge when an agent is worthwhile, when a deterministic workflow is enough and which framework fits your project.

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 choose the right pattern for a task, from a fixed workflow to an autonomous agent.
  • You design tools and context so that agents work reliably and cost-effectively.
  • You implement agents with the Claude Agent SDK, the OpenAI Agents SDK or LangGraph.
  • You set up evaluations and tracing to measure quality and detect regressions.
  • 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 Agent architectures and design patterns
  • Workflows versus agents: prompt chaining, routing, parallelisation, orchestrator-worker
  • The agent loop: plan, act, observe, reflect
  • Multi-agent systems, subagents and handoffs between agents
  • Criteria for choosing model, framework and level of autonomy
Module 2 Tool use and context engineering
  • Function calling and structured outputs with JSON Schema
  • Designing good tools: granularity, naming, error messages, idempotency
  • Connecting and reusing tools via MCP servers
  • Managing the context window: summarisation, compaction, retrieval and prompt caching
Module 3 Frameworks in practice
  • Claude Agent SDK: agent loop, tools, subagents, permissions and hooks
  • OpenAI Agents SDK: agents, handoffs, guardrails and tracing
  • LangGraph: stateful graphs, checkpointing and human-in-the-loop
  • Side-by-side comparison on the same task and criteria for choosing a framework
Module 4 Memory, evals and observability
  • Short-term and long-term memory, storage strategies and forgetting
  • Evaluation datasets, LLM-as-a-judge and task-based success metrics
  • Tracing tool calls, token usage and latency
  • Regression testing when prompts, models or tools change

Day 2

Module 5 Guardrails, security and deployment
  • Protecting against prompt injection, data exfiltration and over-privileged tools
  • Input and output checks, approval steps and sandboxing
  • Deploying as a service, asynchronous execution, retries and cost limits
  • Governance, audit logging and requirements under GDPR and the EU AI Act
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

Solid programming skills in Python or TypeScript, experience with REST APIs and some hands-on experience with LLM APIs or AI assistants. 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

A laptop with Python or Node.js, Git and a code editor, plus API access to at least one LLM provider such as Anthropic or OpenAI. Credentials for the exercises can be provided if needed.

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.

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Thu, 3 December 2026 – Fri, 4 December 2026 Hanover Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Munich Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Online Request
  • Tue, 9 February 2027 – Wed, 10 February 2027 Stuttgart Request

Frequently asked questions

Which agent framework should we use?

It depends on your model strategy, the level of control you need and your team’s skills. Vendor SDKs offer a lean start with tightly integrated features, while LangGraph gives you finer control over state and flow. In the course you solve the same task with several frameworks and derive clear selection criteria for your own project.

How do you stop an agent from taking wrong or dangerous actions?

With several layers of protection: narrowly scoped tools with minimal permissions, input and output validation, approval steps for critical actions, sandboxing and continuous evaluation. Prompt injection via external content is treated as a realistic threat. You practise these measures directly on your own agent.

Does every automation need an agent?

No. Many tasks are handled more reliably, cheaply and transparently by a fixed workflow of a few LLM calls. Agents pay off when the solution path cannot be planned in advance and the system must react flexibly to intermediate results. We train exactly this trade-off using practical examples.

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 “AI Agent Development 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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