Training course · OpenAI Codex · Go

OpenAI Codex for Go Developers

Delegate development work with OpenAI Codex: CLI and cloud agent, AGENTS.md, sandboxing, approvals and team PR workflows.

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

“OpenAI Codex for Go Developers” is designed for roles such as Go developers in back-end and platform teams, Developers of cloud-native tools, operators and CLIs and Site reliability engineers maintaining their own Go code and shows step by step how to use OpenAI Codex productively, safely and transparently in Go – from the fundamentals to advanced workflows.

OpenAI Codex is a coding agent that runs both locally, as a command-line tool and IDE extension, and as a cloud agent. Locally, Codex reads your repository, proposes changes and runs commands inside a sandboxed environment. In the cloud, it works on delegated tasks in parallel inside isolated containers and returns results as traceable diffs or pull requests. Over two days you will learn how to combine both modes effectively and how to scope tasks so that the agent can complete them reliably and with minimal back-and-forth.

Control and security sit at the heart of the course. With AGENTS.md you hand Codex project knowledge, build and test commands, and conventions. You configure approval modes and sandboxing so that the agent only does what you permit, and you set up cloud environments with suitable setup scripts. You will also integrate Codex into your pull request workflows, use automated code review, and define team rules on which tasks are delegated and how results are checked before they are merged into the main branch.

Go is the language behind many cloud-native tools, microservices, CLIs and infrastructure components. Its strengths are simplicity, fast compilation and built-in concurrency – yet goroutines, channels, context handling and error handling all demand discipline. AI coding assistants and agents suit Go particularly well: the compiler, go vet and the race detector provide fast, unambiguous feedback that agents can iterate on. Typical tasks include table-driven tests, gRPC and HTTP handlers generated from specifications, tracking down race conditions or goroutine leaks and refactoring towards clean interfaces. This course shows you how to get idiomatic Go from AI and how to verify the results with Go’s own tooling.

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?

  • Go developers in back-end and platform teams
  • Developers of cloud-native tools, operators and CLIs
  • Site reliability engineers maintaining their own Go code
  • Software architects designing high-performance microservices

Typical challenges we address

  • Subtle concurrency bugs such as race conditions, deadlocks and goroutine leaks
  • Repetitive boilerplate in error handling, handlers and tests
  • Inconsistent project layouts and interface boundaries in growing codebases
  • Difficult performance analysis of memory allocations and latency

What you will be able to do

  • You use Codex locally and in the cloud for the development tasks each mode suits best.
  • You write an AGENTS.md that guides the agent reliably through your project.
  • You configure approval modes and sandboxing to match your security requirements.
  • You delegate tasks to the cloud agent and review the results systematically.
  • Have AI agents produce idiomatic Go code, tests and services that follow Go conventions.
  • Use AI deliberately to analyse concurrency and performance problems.
  • Verify generated code with the compiler, go vet, the race detector, linting and benchmarks.

Course outline

Day 1

Module 1 Codex at a glance: CLI, IDE and cloud
  • Comparing how Codex works locally and in the cloud
  • Installing the Codex CLI, signing in and starting it in a repository
  • Interactive sessions, non-interactive execution and model choice
  • Distinguishing tasks that suit a coding agent from those that do not
Module 2 Project knowledge with AGENTS.md
  • Building an AGENTS.md with build, test and lint commands
  • Stating conventions, architectural notes and prohibitions precisely
  • Using nested AGENTS.md files for monorepos and sub-projects
  • Checking effectiveness and refining instructions iteratively
Module 3 Sandboxing, approvals and security
  • Choosing approval modes deliberately, from read-only to largely autonomous
  • Understanding sandbox boundaries for file system and network access
  • Handling secrets, dependencies from the internet and prompt injection
  • Configuration files for team standards and safe defaults
Module 4 Delegating tasks to the cloud agent
  • Configuring cloud environments with setup scripts and dependencies
  • Writing tasks with a clear goal, context and acceptance criteria
  • Launching several tasks in parallel and comparing the results
  • Using logs and test output to understand the proposed solutions

Day 2

Module 5 PR workflows and code review in the team
  • Turning results into pull requests and requesting follow-up changes
  • Using automated Codex code review on GitHub
  • Embedding Codex in scripts and CI pipelines
  • Setting team rules for delegation, review duties and accountability
Module 6 Practice lab: Go
  • Writing table-driven tests
  • Implementing gRPC and HTTP services
  • Finding concurrency bugs
  • Idiomatic refactoring
  • Profiling performance
  • Building CLIs and tools
Module 7 Data protection, quality & transfer to daily work
  • Go tooling often runs deep in the infrastructure, so keep kubeconfigs, tokens and certificates strictly out of an AI assistant’s context. Check new modules for provenance, licence and known vulnerabilities, for instance with govulncheck, and record dependencies in an SBOM as required under the Cyber Resilience Act for products with digital elements.
  • 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

Writing table-driven tests

The agent generates idiomatic table-driven tests with subtests, test helpers and fakes for interfaces, runs go test with the race detector and adds missing edge cases until coverage is where you want it.

Implementing gRPC and HTTP services

Protobuf definitions or OpenAPI specs become handlers, middleware for logging and authentication, context propagation with timeouts and clean error returns. Integration tests exercise the endpoints against realistic requests and failure cases.

Finding concurrency bugs

The AI analyses race detector output, goroutine dumps and code using channels or mutexes, explains the cause of deadlocks or leaks and proposes fixes using errgroup or context cancellation.

Idiomatic refactoring

Large packages are untangled, small consumer-side interfaces introduced, errors wrapped and checked with errors.Is, and code aligned with golangci-lint rules. Existing tests keep behaviour safe throughout.

Profiling performance

pprof profiles and benchmark results are interpreted, hotspots and unnecessary allocations identified, and optimisations backed by before-and-after benchmarks. Improvements become measurable rather than merely assumed.

Building CLIs and tools

The assistant creates command-line tools with Cobra or the standard library, including configuration, structured logging, cross-compilation and release automation. Tests, help texts and clear command documentation are included.

Possible hands-on projects

Health check aggregator

A Go service that polls the health endpoints of many services concurrently, merges the results and exposes them as JSON and Prometheus metrics.

Log shipper CLI

A lean command-line tool that tails log files, filters entries and ships them in batches to an HTTP endpoint – with backoff and graceful shutdown.

Kubernetes resource report

A CLI built on client-go that reads resource requests and limits across all deployments and presents anomalies in a clear table.

Prerequisites

You have hands-on programming experience, are comfortable with Git and the command line, and know pull-request-based workflows. Hands-on Go experience including goroutines, interfaces and modules, plus a working knowledge of Git and containers.

Technical requirements

Your own laptop with a terminal, Git and a Node.js runtime; a ChatGPT plan with Codex access or an OpenAI API key, plus a GitHub account for the cloud exercises.

Data protection & compliance

Go tooling often runs deep in the infrastructure, so keep kubeconfigs, tokens and certificates strictly out of an AI assistant’s context. Check new modules for provenance, licence and known vulnerabilities, for instance with govulncheck, and record dependencies in an SBOM as required under the Cyber Resilience Act for products with digital elements.

Suggested dates

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

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Tue, 1 December 2026 – Wed, 2 December 2026 Munich Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Online Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Stuttgart Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Online Request
  • Thu, 4 February 2027 – Fri, 5 February 2027 Berlin Request

Frequently asked questions

When should I use Codex locally and when in the cloud?

Locally you work interactively and close to the code, for example when debugging or making exploratory changes where you want to follow every step. The cloud agent suits well-defined tasks that can run independently, such as adding tests or fixing smaller bugs. You can start several of these in parallel and review each one as a pull request.

What belongs in a good AGENTS.md?

A good AGENTS.md explains how the project is built, tested and checked, which conventions apply and which areas must not be touched. It is concise, concrete and kept up to date. Notes on architecture and common pitfalls also help. During the course you write an AGENTS.md for a project of your own and test how it changes the agent’s behaviour.

How safe is it to let Codex run commands?

By default Codex works inside a sandbox with restricted file and network access and asks for approval before risky steps. You decide how much autonomy to grant through approval modes and configuration. In the course you learn to choose these settings deliberately and to limit risks such as prompt injection or running unfamiliar dependencies.

Does AI really write idiomatic Go?

With good context, yes. Without guidance, models sometimes drift towards patterns from other languages, such as large interfaces or excessive abstraction. On the course you capture your Go conventions, linter configuration and example code so the assistant favours simple, idiomatic solutions, and you verify the output with golangci-lint.

Can AI reliably detect race conditions?

AI is a powerful analysis aid but no substitute for the race detector. The combination works best: the agent runs tests with the race detector, interprets the reports and proposes fixes. You learn to set up that loop and to critically assess any suggested synchronisation changes before accepting them.

How much does “OpenAI Codex for Go 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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