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Course Concepts Map

Sources: Antigravity, 2026-07-05 Raw: Course Concepts Map Source

Course Concepts Map

The project covers all 10 course notebooks (Days 1a through 5b).

Day Notebook Implementation Wiki page
1a Foundational models llm.py — tiered model registry with retry/backoff [[Model Registry]]
1b Multi-agent systems orchestration.py — SequentialAgent pipelines, LoopAgent [[Agent Architecture]]
2a Agent tools & MCP tools.py, mcp_server/ — clinical tools, FastMCP server [[MCP and A2A]]
2b Agent-as-tool & HITL orchestration.py, human_in_the_loop.py — LongRunningFunctionTool [[Human-in-the-Loop Approval]]
3a Memory & state memory.py — session/memory factories, state prefixes [[Memory Layers]]
3b Context engineering context.py — token budgeting, compaction, boundary injection [[Memory Layers]]
4a Observability observability.py, plugins.py — OpenTelemetry, Cloud Trace, structured logging [[Observability]]
4b Evaluation eval/ — ADK EvalSet, tool trajectory + response match scoring [[Testing and Eval]]
5a Agent2Agent (A2A) a2a_server.py — ASGI A2A server with agent card [[MCP and A2A]]
5b Deployment deployment/ — Cloud Run, Vertex AI Agent Engine, GKE [[Deployment]]

Evaluation rubric alignment

Category Points Implementation
Technical Implementation 50 Multi-agent pipelines, MCP, 3-layer security, 4-layer memory, Pydantic, observability
Documentation 20 README, inline docstrings, architecture docs, this wiki
Core Concept & Value 10 Clinical intelligence with inspectable execution, tool, and evidence traces
Video Demo 10 End-to-end workflow demonstration
Writeup 10 Problem-solution-architecture-journey articulation

[!note] Capstone requirement Must demonstrate at least 3 of: ADK multi-agent, MCP server, Antigravity, security, deployability, Agents CLI. Clinical AI Kit demonstrates multi-agent ([[Agent Architecture]]), MCP ([[MCP and A2A]]), security ([[Security Layers]]), and deployability ([[Deployment]]).