AI Agents · Course resources
Notes: Compare AI Agent Types (Training, Human Support and Multi-Agent Systems)
Review the key ideas from Compare AI Agent Types (Training, Human Support and Multi-Agent Systems).
Section 8 asks what can change later agent behaviour and who contributes to the work. The learner distinguishes explicit rules from learned patterns, sees the human preparation behind training, accounts for live human assistance, tests a reusable correction, and compares one agent with a multi-agent arrangement.
| Lecture | Understanding developed |
|---|---|
| Who Chooses an AI Agent's Actions? (Rules vs Machine Learning) | The same visible action may come from a supplied condition or a pattern learned from examples. A product may combine both mechanisms, and the mechanism determines where a correction belongs. |
| Who Teaches an AI Model? (Training Data and Human Work) | People choose, perform, label and review training examples. Coverage and label quality matter alongside the number of examples. |
| Who Helps When a Robot Can't Finish? (Teleoperation) | Teleoperation lets a remote person assist a live job. A buyer still needs evidence about access, waiting, unfinished work and cost. |
| What Are the 5 Common AI Agent Types? | Reflex, model-based, goal-based, utility-based and learning designs name properties that can combine rather than exclusive products or autonomy levels. |
| Does Correcting AI Change Future Answers? (Memory and Instructions) | A saved prompt can carry exact wording into a later task without retraining. Product memory and personalisation require their own product-specific tests. |
| How Do You Reuse AI Corrections? (Saved Rules and Testing) | A reusable correction needs a task scope, exact rule, original evidence and two tests. The fictional practice checks mixed evidence status first, then an all-confirmed boundary case. |
| When Do Several AI Agents Help? (Multi-Agent and Orchestration) | Multiple specialists help when distinct tools or independent work justify the handoffs. Dependent stages, corrections and added checking determine whether one agent remains simpler. |
The new “Your reusable correction rule” entry is proposed from the teaching need and has no inferred publication state. The Weave description and existing source qualifications remain unchanged.