Training course · Windsurf · Tech Leads & Engineering Managers

Windsurf for Tech Leads and Engineering Managers

Develop with the Windsurf AI IDE and its Cascade agent: flows, memories, rules, workflows and safe use across the team.

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 Windsurf with the everyday processes of Tech Leads & Engineering Managers. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Windsurf is an AI-powered development environment built around its agent, Cascade. Cascade understands the structure of your project, follows your actions in the editor and terminal, and works with you according to the flow principle: you and the agent take turns on the same task without having to re-explain context over and over. In this two-day course you will learn how to use Cascade effectively for features, debugging and refactoring, and how to make the most of intelligent completion in your daily development work.

To keep results consistent across a team, you steer Cascade with global and workspace rules, rely on memories for lasting project knowledge and automate recurring procedures with workflows that run from a slash command. You will connect external tools through MCP, control how terminal commands are executed and decide which data Cascade is allowed to see. Finally, you will define rules for code review, security and data protection so that Windsurf can be used responsibly within your organisation and its existing 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 use Cascade productively in flow for features, debugging and refactoring.
  • You steer Windsurf’s context precisely and keep sensitive content protected.
  • You maintain memories and rules that reliably enforce your project conventions.
  • You automate recurring procedures with workflows and connect external tools via MCP.
  • 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 Getting to know Windsurf and Cascade
  • Installation, importing existing editor settings and basic configuration
  • Cascade in code and chat mode: when the agent edits and when it only advises
  • The flow principle: how Cascade takes your editor and terminal actions into account
  • Intelligent completion and inline commands in everyday development
Module 2 Steering context
  • Understanding codebase indexing and referencing relevant files or directories
  • Bringing documentation, web sources and terminal output into context
  • Excluding sensitive content from context with ignore files
  • Structuring sessions and starting afresh when the topic changes
Module 3 Memories and rules for consistent results
  • Reviewing and maintaining auto-generated and manually created memories
  • Creating and versioning global and workspace rules
  • Expressing architecture, style and testing guidelines as effective rules
  • Activating rules selectively by file pattern or occasion
Module 4 Workflows and automation
  • Defining recurring procedures as workflows and launching them via slash command
  • Building workflows for release preparation, testing or code maintenance
  • Connecting MCP servers for issue trackers, databases or internal services
  • Controlling terminal command execution with allow and deny lists

Day 2

Module 5 Quality, review and governance
  • Tracing Cascade’s changes, accepting them partially or reverting them
  • Using a test-driven approach and preview features for verification
  • Team settings, data protection options and handling of secrets
  • Defining review processes and accountability for AI-generated code
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 program regularly in at least one language and are familiar with Git and a modern IDE. 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 the Windsurf editor, Git and your usual toolchain installed; a Windsurf account with access to Cascade is required.

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.

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Mon, 30 November 2026 – Tue, 1 December 2026 Hanover Request
  • Wed, 16 December 2026 – Thu, 17 December 2026 Online Request
  • Tue, 12 January 2027 – Wed, 13 January 2027 Online Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Munich Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Online Request

Frequently asked questions

What does Windsurf mean by flows?

Flows describe the close collaboration between you and Cascade. The agent observes what you do in the editor and terminal and takes those actions into account in its next steps. As a result you do not have to keep explaining where you are, and you can move smoothly between doing the work yourself and delegating it to the agent.

How do memories, rules and workflows differ?

Memories store knowledge that Cascade has gathered while working or that you have explicitly recorded. Rules are guidelines you write that shape the agent’s behaviour permanently. Workflows are reusable sequences of steps for recurring tasks, launched with a slash command. Together they keep results consistent across the whole team.

Can I use Windsurf with my existing projects and languages?

Yes. Windsurf is language-agnostic and works with your existing toolchain, including build tools, package managers and test frameworks. Whether you work on back end, front end, data processing or infrastructure code, what matters is giving Cascade good rules and clear context. Each course variant adds the specific focus for your field.

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 “Windsurf 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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