Matt Pocock – Software Factories for Real Engineers

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Matt Pocock – Software Factories for Real Engineers

Move Beyond AI-Assisted Coding

AI coding agents can generate code, fix bugs, and complete development tasks at remarkable speed.

However, faster output does not automatically mean better software.

Without clear specifications, architectural guidance, testing standards, and review processes, autonomous agents can introduce technical debt

and make an existing codebase increasingly difficult to maintain.

Software Factories for Real Engineers addresses this challenge by teaching developers how to apply established software engineering principles

to AI-powered development workflows.

The program introduces the concept of a Software Factory: an engineered system where coding agents can investigate tasks, implement changes,

run checks, and return reviewable results with less continuous human supervision.

Instead of spending the entire day correcting AI-generated code, engineers learn to design the environment and processes

that help agents produce more reliable work.

The Real Engineering Principles Behind Software Factories

The course draws on established engineering concepts rather than treating AI coding as an entirely new discipline.

Ideas from Domain-Driven Design, modular architecture, testing, and software design are adapted to environments

where AI agents perform a growing share of implementation work.

Its central framework connects five important elements:

The Team: Human engineers who provide strategic direction, domain knowledge, and engineering judgment.

The Agent: The AI model and coding harness responsible for executing development tasks.

Knowledge: Documentation, architecture, specifications, tests, and shared terminology that give agents the context they need.

Brakes: Automated checks, code review, and quality controls that prevent unreliable changes from moving forward.

Infrastructure: Data sources, scripts, sandboxes, schedules, and triggers that allow agents to work with greater independence.

The key principle is that automation should not come before quality control.

Developers first establish the knowledge and safeguards that make autonomous execution trustworthy, then introduce mechanisms that increase speed and scale.

Hands-On Learning with a Practical Codebase

Rather than asking participants to experiment immediately on critical production projects, the course provides an example codebase

and a safe sandbox environment for practice.

The learning process moves through several stages:

Explore and document the structure of a project.

Define the rules and standards that guide agent behavior.

Create specifications and divide work into manageable tickets.

Introduce automated checks and review mechanisms.

Connect agents to the infrastructure needed to execute tasks.

Design a customized Software Factory for a real development environment.

By the end, participants should have a clearer understanding of how to build a system that supports their own engineering standards,

team structure, and codebase requirements.