If you think all of this is about an agent that automates test cases, my class is a waste of your time.
The future of the economy is either automating or augmenting with agents. Automating means the agent does the job. Augmenting means the agent makes a person faster at it. Customer service, financial analysis, stock market trading, bookkeeping: some of it can be fully automated, and the rest augmented.
Look at QuickBooks. Accountants do practically their whole job inside that software. Now say you are a test automation engineer at Intuit, the company behind QuickBooks, and all day you automate exactly what a bookkeeper does, in the test environment.
Your automation already clicks through everything a bookkeeper does, every day, in the test environment. What is the difference between that and doing the bookkeeper's job?
Two things, and you already hold the harder one. Your scripts follow steps, while a bookkeeper makes judgments, and judgment is what the model behind an agent adds. And your scripts run in a test environment on fake data. The bookkeeper's job runs in production on real money, which is exactly where everything in this book becomes necessary: the context, the gates, the sandbox, the log, and a person who answers for it. You already know every screen and every step. Once you know how to build an agent for the software testing life cycle, nothing is preventing you from building one for development, for security testing, for bookkeeping, for financial analysis, for anything. OpenAI and Anthropic hire for exactly this, and the title is forward deployed engineer. You walk into a company, make an inventory of its activities, maybe three hundred and sixty-five of them in a spreadsheet, and decide which ones an agent automates and which ones it augments. Start with one for yourself, a financial analyst for your own money.