Training course · AI Agent Development · Mobile Apps

AI Agent Development for Mobile Developers

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

“AI Agent Development for Mobile Developers” is a hands-on course for roles such as iOS developers using Swift and SwiftUI, Android developers using Kotlin and Jetpack Compose and Cross-platform developers using Flutter or React Native. Rather than learning AI Agent Development in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Mobile Apps.

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.

Mobile development spans several worlds: Swift and SwiftUI for iOS, Kotlin and Jetpack Compose for Android, and cross-platform approaches with Flutter or React Native. On top come store guidelines, device fragmentation, offline capability, push notifications and demanding release processes. AI coding assistants and agents relieve teams of precisely these tasks: they build screens from designs, migrate UIKit or XML layouts to declarative UI frameworks, write UI and unit tests, analyse crash reports and automate build pipelines with Fastlane. Because mobile projects depend heavily on platform conventions, the AI needs precise context on architecture, state management and target platforms. This course shows you how to build that context and verify results on real devices.

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?

  • iOS developers using Swift and SwiftUI
  • Android developers using Kotlin and Jetpack Compose
  • Cross-platform developers using Flutter or React Native
  • Mobile teams in agencies and product companies
  • Tech leads responsible for app architecture and releases

Typical challenges we address

  • Maintaining several platforms in parallel with different languages and UI paradigms
  • Migrating from UIKit or XML layouts to declarative UI frameworks
  • Hard-to-reproduce crashes and performance issues across a wide range of devices
  • Laborious build, signing and store release processes

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.
  • Use AI agents for screens, UI migrations and tests in native and cross-platform apps.
  • Diagnose crashes and performance issues faster and automate build and release processes with AI support.
  • Give the AI clear platform conventions and architecture rules and verify results on real devices.

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: Mobile Apps
  • Turning designs into screens
  • Declarative UI migration
  • Testing mobile apps
  • Analysing crash reports
  • Offline capability and data storage
  • Build and release automation
Module 7 Data protection, quality & transfer to daily work
  • Apps often handle sensitive device data such as location, contacts or health information, so generated code must request permissions sparingly and manage consent in line with GDPR. Signing keys, API keys and store credentials do not belong in prompts, and new SDKs and packages should be checked for licensing, tracking behaviour and supply-chain risk.
  • 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

Turning designs into screens

From screenshots or design specs, the assistant generates SwiftUI, Compose, Flutter or React Native screens that reuse your components, themes and navigation and respect dark mode and dynamic type.

Declarative UI migration

The agent moves UIKit view controllers or Android XML layouts to SwiftUI or Jetpack Compose step by step, wraps legacy parts via interop layers and protects behaviour with snapshot and UI tests.

Testing mobile apps

The AI writes unit tests for view models and repositories, UI tests with XCTest, Espresso or Flutter widget tests, and sets up mocks for networking and persistence.

Analysing crash reports

Symbolicated stack traces from crash reporting services are interpreted, likely causes such as threading or lifecycle issues narrowed down and fixes proposed alongside reproducing tests.

Offline capability and data storage

The assistant designs local persistence with Room, Core Data, SwiftData or SQLite, sync logic with conflict handling and robust networking layers with retries and caching.

Build and release automation

Fastlane lanes, Gradle configuration and CI workflows for builds, signing, beta distribution and store uploads are created or repaired, including versioning and release notes. Expiring certificates are flagged early.

Possible hands-on projects

Stocktake app with barcode scanner

A Flutter or React Native app that scans barcodes, records items offline and syncs them with a REST API once a connection is available.

SwiftUI habit tracker

An iOS app with SwiftUI, local persistence, widgets and local notifications, developed entirely with an AI agent. Habits and progress are displayed in clear charts.

Compose feedback form

An Android app built with Jetpack Compose that captures forms with photo attachments, caches them offline and submits them to a back end – with UI tests.

Prerequisites

Solid programming skills in Python or TypeScript, experience with REST APIs and some hands-on experience with LLM APIs or AI assistants. Hands-on app development experience with Swift, Kotlin, Dart or React Native, plus Xcode or Android Studio and Git.

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

Apps often handle sensitive device data such as location, contacts or health information, so generated code must request permissions sparingly and manage consent in line with GDPR. Signing keys, API keys and store credentials do not belong in prompts, and new SDKs and packages should be checked for licensing, tracking behaviour and supply-chain risk.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Bremen Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Online Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Salzburg Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Online Request
  • Tue, 9 February 2027 – Wed, 10 February 2027 Kaiserslautern 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.

Does the course cover iOS, Android and cross-platform equally?

Yes. Working with AI assistants follows the same principles on every platform: provide context, scope tasks and test the results. In the exercises you choose your focus – Swift, Kotlin, Flutter or React Native – and tackle typical tasks for your platform, so mixed teams benefit just as much as specialists.

Can AI work with Xcode and Android Studio?

Many AI assistants can be used inside Android Studio or alongside Xcode, and agents also work from the command line with xcodebuild, Gradle or the Flutter CLI. We show you how to drive builds, tests and simulators so that the agent can check its own results and fix errors iteratively.

How much does “AI Agent Development for Mobile Developers” 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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