# Agent-First Software Engineering Workshop

A multi-module workshop that equips your engineering team with the practical knowledge and disciplined workflows to use AI as a genuine force multiplier in software development.

## Why This Workshop

### Turn AI into a real engineering advantage

Large language models have already changed the nature of software development, but using them without understanding how they work - or without a disciplined approach to validating their output - results in messy code that creates more problems than it solves. Our workshop builds real intuition for how these models work and pairs it with field-tested techniques for context engineering, defensive development, and scaling AI use across engineering teams.

- Build Real AI Intuition  
- Field-Tested Techniques  
- Hands-On Workflows  
- Scalable Practices

## Workshop Modules

### Three modules from fundamentals to applied workflows

Our workshop is structured as three progressive modules that take your team from prompting fundamentals through AI-powered engineering practices to hands-on applied development workflows in agentic IDEs.

#### 01

**Module 1: Prompting Fundamentals**  
Build a foundation of practical knowledge about how LLMs work and how to use them effectively. Covers LLM architecture essentials, core principles of AI use, actionable prompt techniques, context engineering strategies, and an iteration pattern for reliably producing high-quality AI output. Designed for anyone who uses AI, at all skill levels.

#### 02

**Module 2: AI-Powered Engineering**  
Learn how AI tools integrate into real engineering workflows and how agentic AI systems work under the hood. Covers AI-assisted code review, test generation, documentation, orchestration patterns, the agent extension ecosystem including tools, skills, and MCP, and safety guardrails for production-ready AI-assisted processes.

#### 03

**Module 3: Applied AI Workflows**  
Bring everything together with hands-on development workflows in agentic IDEs. Covers the primitives that make agentic IDEs fundamentally different from chat interfaces, defensive development practices, codebase setup, Git integration, and workflow patterns for directing AI assistants with confidence in enterprise codebases.

## Explore Our Workshop Modules

### Module 1: Prompting Fundamentals

The introductory module provides a balance of theoretical and practical knowledge that sets attendees on the path to becoming AI power users. By demystifying the internals of large language models in a way that is approachable for both engineers and business users, we lay the foundations for building true intuition about effective AI use. Attendees walk away with a deeper understanding of the technologies they use every day, a toolkit of actionable prompting techniques, and the curiosity to continue building on these foundations. Covers LLM architecture essentials, principles of AI use, prompt techniques, context engineering, the iteration pattern, and intuitive debugging. Duration is 2-3 hours over 1-2 sessions, appropriate for all skill levels.

### Module 2: AI-Powered Engineering Practices

Building on the prompting foundations from Module 1, this module focuses on how AI tools integrate into real engineering workflows and how agentic AI systems work under the hood. We cover AI-assisted code review, test generation, documentation, orchestration and system patterns, agents, tools, skills, and MCP, plus safety and guardrails - all demonstrated through live, interactable workflows. Optional deep-dives cover RAG techniques, advanced search and retrieval, agentic memory, and validation at scale. Duration is 2-3 hours over 1-2 sessions, designed for software developers, data engineers, AI/ML engineers, infrastructure engineers, and technology leadership.

### Module 3: Applied AI Development Workflows

This module brings together everything from Modules 1 and 2 into hands-on, applied development workflows using agentic IDEs. We demystify the primitives that make agentic IDEs fundamentally different from chat interfaces - skills, MCP servers, instruction files, hooks, and custom agents - and show how these building blocks are the durable investment that outlasts any single platform. Participants work directly in AI-powered development environments, applying defensive development practices and disciplined workflow patterns to build, test, and ship code with AI assistance at every step. Duration is 3-4 hours over 1-2 sessions, designed for software engineers comfortable in high-code environments.

### Ready to accelerate your team's AI workflows?

Interested in working with us?

[Talk to an expert](/content/contact/index.html)

**Components of a RAG Chatbot**  
[RAG LLM architectures can be complex, but there's a lot of overlap among common use cases](/content/blog/components-of-rag-chatbot/index.html)

**Automating Manual Data Extraction**  
[Leveraging the best of humanity and automation while keeping it easy on developers](/content/blog/automating-manual-data-extraction/index.html)

**Images in RAGs**  
[Describing, retrieving, and learning from images in a Retrieval Augmented Generation chatbot](/content/blog/images-in-rags/index.html)