Training course · AI Agent Development · C/C++ & Embedded

AI Agent Development for C/C++ and Embedded 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 C/C++ and Embedded Developers” is a hands-on course for roles such as Embedded software engineers working with microcontrollers and RTOS-based systems, C++ developers in systems software, automotive and industrial automation and Firmware and device driver developers. Rather than learning AI Agent Development in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from C/C++ & Embedded.

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.

In embedded and systems development, determinism, memory safety and tight resource budgets matter more than almost anywhere else. AI coding assistants and agents help you read datasheets and register maps, write drivers and hardware abstraction layers, clear compiler warnings and static analysis findings, and build unit tests with mocks for hardware access. The domain also demands particular care: undefined behaviour, race conditions in interrupt handlers or MISRA violations creep in quickly when generated code is accepted uncritically. In this course you learn to integrate AI into your cross-compilation workflow with CMake, debuggers and hardware-in-the-loop tests while meeting the quality and safety expectations of functionally safe products.

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?

  • Embedded software engineers working with microcontrollers and RTOS-based systems
  • C++ developers in systems software, automotive and industrial automation
  • Firmware and device driver developers
  • Embedded Linux and Yocto developers
  • Test and verification engineers for embedded systems

Typical challenges we address

  • Memory errors, undefined behaviour and concurrency bugs are hard to find and costly in the field
  • Legacy C codebases full of global state and macros with little test coverage
  • Significant effort to comply with and evidence coding standards such as MISRA C/C++ or AUTOSAR C++
  • Hardware knowledge locked in datasheets and in the heads of a few experts

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.
  • Apply AI agents to driver development, refactoring and debugging in C and C++ in a targeted, verifiable way.
  • Raise test coverage of low-level software with generated unit tests and mocks that run on the host.
  • Combine AI support with coding standards, static analysis and the evidence requirements of safety-related development.

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: C/C++ & Embedded
  • Drivers from datasheets
  • Making sense of static analysis
  • Unit tests with hardware mocks
  • Crash and memory analysis
  • Introducing modern C++
  • Build and toolchain upkeep
Module 7 Data protection, quality & transfer to daily work
  • Firmware is often core intellectual property and may be subject to export control or security requirements: you learn how to protect source code, assess licensing risks in generated code and reconcile AI use with the Cyber Resilience Act, IEC 62443 and functional safety under ISO 26262 or IEC 61508.
  • 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

Drivers from datasheets

Have register descriptions and timing diagrams structured from datasheets, then derive headers with bitfield definitions, initialisation routines and a lean hardware abstraction layer for SPI, I²C or UART peripherals.

Making sense of static analysis

Explain and prioritise findings from clang-tidy, cppcheck or MISRA checkers, propose compliant corrections and prepare justified deviations for your deviation records. Recurring patterns can then be fixed across the codebase.

Unit tests with hardware mocks

Extract testable interfaces from low-level code, generate mocks for register and HAL access and create unit tests with GoogleTest, Unity or CppUTest that run on the host.

Crash and memory analysis

Interpret HardFault registers, stack traces, core dumps and sanitiser output, narrow down likely causes such as stack overflows or use-after-free and suggest targeted debugging steps.

Introducing modern C++

Gradually replace raw pointers, manual memory management and macros with RAII, constexpr, smart pointers and type-safe abstractions without degrading real-time behaviour or the memory budget.

Build and toolchain upkeep

Structure CMake projects for cross-compilation, create toolchain files, linker scripts and CI jobs for firmware builds and analyse build failures in the context of the target platform.

Possible hands-on projects

Register header generator

A script that converts register tables from an SVD or CSV file into type-safe C headers with masks, offsets and accessor functions.

Serial protocol monitor

A desktop tool that reads from a serial port, decodes a custom binary protocol and displays frames with timestamps and filters.

MISRA report dashboard

A small web view that aggregates static analysis results, groups rule violations by module and tracks progress across builds. New violations are highlighted.

Prerequisites

Solid programming skills in Python or TypeScript, experience with REST APIs and some hands-on experience with LLM APIs or AI assistants. You are proficient in C or C++ and experienced with build systems, debugging and low-level or systems programming.

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

Firmware is often core intellectual property and may be subject to export control or security requirements: you learn how to protect source code, assess licensing risks in generated code and reconcile AI use with the Cyber Resilience Act, IEC 62443 and functional safety under ISO 26262 or IEC 61508.

Suggested dates

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

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Wed, 2 December 2026 – Thu, 3 December 2026 Munich Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Online Request
  • Thu, 7 January 2027 – Fri, 8 January 2027 Hanover Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Online Request
  • Thu, 4 February 2027 – Fri, 5 February 2027 Berlin 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.

Is AI-generated code acceptable in safety-critical embedded systems?

Standards such as ISO 26262 or IEC 61508 do not prohibit AI tools, but they require traceable processes, reviews, testing and, where relevant, tool qualification. Generated code is therefore treated like any other code: reviewed, tested and verified against coding standards. In the course you develop working practices that support this evidence rather than bypass it.

Can an AI agent work with my target hardware?

Agents can trigger builds, flash devices, read serial output and run tests on the host or a hardware-in-the-loop rig, provided you expose the right command-line tools. We show you how to set up such loops in a controlled way, with clear permissions and without the agent accessing production devices unsupervised.

How much does “AI Agent Development for C/C++ and Embedded 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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