Coding with AI is as useful as you make it. Autocomplete is using AI. Prompting into a chat-window in your IDE is using AI. Having the agent build you tests is using AI. All of these are helpful. None of them will 10x what you’re able to deliver.
We’re going to take the next step here and learn how to engineer the agentic harness that you’ll then use in your own work. Directly and reliably. We’ll start with a simple manual harness, and we’ll incrementally build it into a system that coordinates specialized agents, validates their work, captures lessons from every run, and scales toward unattended execution.
Each lesson builds on the previous one, transforming a naive implementation into a robust, evolving system you can continue improving long after the course ends.
Along the way, you’ll discover why reliable AI isn’t about finding the perfect prompt – it’s about engineering the process that surrounds every model interaction.
Actual practice through a Java project – definitely the best way to understand agentic harnesses
The essential guide to engineering the workflow that guides multiple agents toward reliable outcomes. The 3 modules build an agentic AI harness one layer at a time: from a naive workflow, to mechanical enforcement, to autonomous execution.
Everything you need to understand agentic AI workflows.
The lessons in this course are text-based, with a full project backing each one.
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