Week 12 · overview
Week 12: Multi-Agent Systems and Responsible AI
Multi-agent behavior depends on individual capabilities, shared protocols, messages, environmental feedback, and authority boundaries. Responsible AI is therefore a system practice: risks and accountability live across the entire chain, not inside one model or one agent.
| Day | Role | Evidence |
|---|---|---|
| 1 | Lesson 1: Multi-agent systems | Trace agents, environments, communication, and emergence |
| 2 | Lesson 2: Responsible AI | Connect fairness, safety, privacy, inclusion, transparency, and accountability |
| 3 | Investigation | Audit a message envelope and human-authorization gate |
| 4 | Lab | Compare verified, schema-mismatch, and unauthorized handoffs |
| 5 | Assessment + Reflection | Repair a coordinated-agents-means-responsible claim |
Your Multi-Agent Handoff Audit Record preserves actors, messages, schemas, evidence, capabilities, authority checks, outcomes, claim repair, and limitation. The Lab is a deterministic trace viewer and sends no messages.
The complete pinned Microsoft source and credits remain in upstream/.
Review the source record.
Chapter question
How does Multi-Agent Systems and Responsible AI change what evidence a student should trust?
What you will understand
You will connect the named mechanism to the evidence it can and cannot support.
What you will do
You will inspect supplied cases, trace the important calculation, and test one bounded claim.
What you will produce
You will produce the week evidence record named in the Lab or reflection.
How the week connects
This week carries the previous AI mechanism forward into a stricter evidence check.
Key vocabulary
mechanism; evidence; boundary; test; claim
Approximate time
Five class meetings at about 210 instructional minutes total.
Final evidence required
Submit the completed week record with at least two exact labels from the artifact.