Training course · Agentic Coding · Tech Leads & Engineering Managers

Agentic Coding for Tech Leads and Engineering Managers

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 Tech Leads and Engineering Managers” is designed for roles such as Tech leads and lead developers, Engineering managers and software team leads and Heads of engineering and CTOs in mid-sized companies and shows step by step how to use Agentic Coding productively, safely and transparently in Tech Leads & Engineering Managers – from the fundamentals to advanced workflows.

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.

AI coding assistants and autonomous agents are changing how engineering teams work: tasks are sliced differently, effort shifts from writing code to reviewing it, and deciding which work can be delegated to agents becomes a leadership responsibility. Tech leads and engineering managers have to select tools, set policies, enable their teams and measure impact without losing sight of code quality, security and the growth of junior engineers. In this course you learn to plan the rollout of AI coding tools in a structured way, establish shared working practices and rule files for your team, choose meaningful metrics beyond raw speed and apply AI to leadership tasks such as planning, onboarding and technical communication. Deep programming skills are not required.

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?

  • Tech leads and lead developers
  • Engineering managers and software team leads
  • Heads of engineering and CTOs in mid-sized companies
  • Delivery and project leads in IT organisations
  • Developer experience and enablement leads

Typical challenges we address

  • Unclear policies lead to a patchwork of tools and shadow AI within the team
  • More generated code increases review load and the risk of technical debt
  • Difficulty measuring the real benefit of AI tools in a credible way
  • Concerns about junior engineers’ skill development and team buy-in

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.
  • Plan the rollout of AI coding tools in a structured way with clear policies, roles and guardrails.
  • Measure the impact on delivery, quality and satisfaction with credible metrics.
  • Use AI agents yourself for leadership tasks such as planning, onboarding and technical communication.

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: Tech Leads & Engineering Managers
  • Writing a team AI policy
  • Rule files and team conventions
  • Measuring impact
  • Slicing work for agents
  • Faster onboarding
  • Technical communication
Module 7 Data protection, quality & transfer to daily work
  • As a leader you own the legal framework: you learn to address licensing and copyright questions around generated code, protection of source code and secrets, works council co-determination rights when measuring performance and obligations under the GDPR and the EU AI Act in your team policies.
  • 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 a team AI policy

Draft a practical guideline that clearly defines approved tools, permitted data classes, review obligations for generated code, handling of secrets and accountabilities, illustrated with concrete examples.

Rule files and team conventions

Build shared context files for coding agents covering architectural principles, testing strategy, style rules and banned patterns, keep them under version control and ensure consistent output across the team.

Measuring impact

Collect metrics such as DORA measures, pull request cycle time, change failure rate and developer satisfaction before and after rollout, and prepare the analysis with AI support.

Slicing work for agents

Write and break down backlog items so they can be delegated to agents with clear acceptance criteria, and decide which tasks deliberately stay with people.

Faster onboarding

Prepare codebases, architecture decisions and operational knowledge for new team members, create guided exploration tasks and have questions about the codebase answered with source references.

Technical communication

Draft incident post-mortems, roadmap updates, decision papers for senior management and release notes from tickets and commits, structured and tailored to each audience. This frees up time for actual leadership.

Possible hands-on projects

Team metrics dashboard

A small web app that pulls data from your Git platform and shows cycle times, review duration and deployment frequency over time.

PR summariser

A bot that collects all merged pull requests each week and turns them into a readable status report for stakeholders.

Onboarding guide generator

A script that analyses repository structure, README files and ADRs and produces a structured onboarding plan with learning tasks. It adapts to role and seniority.

Prerequisites

You have several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. You lead a software team, either technically or as a line manager, and understand the development process; current hands-on coding is helpful but not required.

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

As a leader you own the legal framework: you learn to address licensing and copyright questions around generated code, protection of source code and secrets, works council co-determination rights when measuring performance and obligations under the GDPR and the EU AI Act in your team policies.

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
  • Wed, 16 December 2026 – Thu, 17 December 2026 Online Request
  • Mon, 11 January 2027 – Tue, 12 January 2027 Online Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Augsburg Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online 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.

How do we tell whether AI coding tools are worth it?

Don’t count generated lines of code; look at outcomes: lead time from idea to production, change failure rate, review effort, defect rates and team satisfaction. A baseline before rollout and accompanying qualitative surveys are essential. In the course you design a lean measurement approach that fits your organisation and involves employee representatives.

Do junior engineers still learn enough when AI writes the code?

The concern is valid and can be managed. Proven practices include having engineers explain every generated section in review, deliberate practice phases without an assistant and pairing with experienced colleagues where AI acts as a learning partner. You learn to adapt learning paths and review culture so that skill building and productivity go hand in hand.

How much does “Agentic Coding for Tech Leads and Engineering Managers” 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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