Agent Concepts for Product and Project Leads
Understand agents well enough to scope, plan and review an agent project with your engineers
A taste of a lesson
The demo of our booking agent works perfectly. Engineering says three more months. Is that padding?
Probably not. A demo proves the happy path: a clear request, available slots, no surprises. Production brings vague requests, double bookings, cancelled flights, people changing their minds and inputs nobody planned for. Most of the remaining time usually goes into building a test set of realistic cases, fixing failures and adding safe fallbacks to a person. A good question to ask them is: what is our evaluation set, and what pass rate do we need before a pilot? Could you list five awkward booking requests a real customer might send?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Describe the parts of an agent and why its behaviour varies between runs
- Write a narrow, measurable scope for an agent feature with clear limits
- Plan timelines that account for evaluation and edge cases, not just the demo
- Ask the right questions at scoping, testing, launch and monitoring stages
Lesson plan
- 1 What an agent is made of Name the parts of an agent and what each contributes, in plain language. Start
- 2 Scoping an agent feature Write a narrow scope with measurable success and explicit limits. Start
- 3 Why demos mislead Understand the gap between a working demo and a reliable product. Start
- 4 Cost and risk in plain numbers Reason about running costs and the main risks without technical depth. Start
- 5 Launching and watching Plan a careful rollout with fallback, logging and an owner for failures. Start
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About this tutor
For product managers, project leads and founders who will not build agents themselves but need to make good decisions about them. You will learn what agents are made of, why they behave less predictably than ordinary software, what drives their cost and timelines, and which questions to ask at each stage: scoping, design, testing, launch and monitoring. Lessons use plain language and realistic project scenarios. By the end you can write a sensible scope for an agent feature, spot risky assumptions early and hold a useful conversation with engineers and vendors.
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About the teacher
I teach how AI agents are built: the loop, the tools, the memory, and when a plain workflow is the better choice
9 tutors 310 lessons taught Sample
I build and teach the inner workings of AI agents. Most of my working life has been spent on backend systems, so I approach agents the way I approach any distributed system: what runs, in what order, what can fail, and what it costs. I like to start every topic with a drawing of the loop on a whiteboard and...
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