Building AI Agents That Actually Work
Most AI agents impress in a demo, then fall apart on real work. Build one that remembers, uses your tools, and runs a job the same way every time.
About this course
The gap between an agent that demos well and one you actually trust is memory, tools, and a repeatable process. This course closes it.
Most people prompt a chatbot, get one good answer, and call it an agent. Then it forgets what it learned yesterday, has no access to the systems where the work lives, and improvises a different approach every run. You start with how an agent actually thinks: the agent loop that turns a goal into action. From there you set up a real workspace, write context files that onboard your agent like a new hire, and wire a self-improving loop so it keeps what works.
The back half is integration and repeatability. You connect outside tools through MCPs, run multi-tool workflows end to end, and write Skills: documented SOPs that make your agent perform a task the same reliable way every time. The final lesson puts the whole system together on one job.
You walk away with a working agent that has its own workspace, context, memory, connected tools, and a library of Skills, plus the repeatable method to point it at any new task.
Who it's for
Founders: want an agent that handles a recurring job using their real tools, not a one-off chat reply.
Operators: turn the SOPs in their head into Skills an agent can run reliably every time.
Builders: move past prompt tricks to MCP tool connections and multi-tool workflows that hold up on real work.
Course Curriculum
Building AI Agents That Actually Work
What you'll learn
- From Chat to Agents
- How Agents Think — The Agent Loop
- Setting Up Your Workspace
- Context Files — Onboarding Your Agent
- The Self-Improving Loop — Agent Memory