Build a dependable agent workflow with scoped context, approved tools, tested skills, and human review for consequential actions.
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.
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.
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