Introduction to Gemini Enterprise Agent Platform for Python Developers
Developer-first introduction to Gemini on the Gemini Enterprise Agent Platform using the google-genai SDK. Build real Python apps with multimodal prompts, streaming, thinking mode, safety settings, and structured JSON output. Ends with a capstone merch-desk CLI.
View badge details
About This Course
The first course in a four-level Google AI learning path for developers. Go from "never touched Google Cloud" to calling the latest Gemini models on the Gemini Enterprise Agent Platform in your own code. You will set up Application Default Credentials against a real GCP project, use the unified google-genai Python SDK to call Gemini 3.1 Flash / Gemini 3.1 Pro with streaming and thinking mode, send multimodal prompts (images, PDFs, audio) backed by Cloud Storage, lock down safety settings, and enforce structured JSON output. Every lesson pairs a curriculum-rich AI-led teaching lesson with a hands-on Python lab that ships as a starter project in browser-based VS Code. You finish with a capstone CLI a merchandising assistant that fuses multimodal input, system instructions, safety settings, and JSON schemas ready to build on in Course 200.
Course Curriculum
12 Lessons
Google Cloud AI — The Lay of the Land
Agent-led tour of Google Cloud's AI stack. Orient on the Gemini Enterprise Agent Platform (formerly Vertex AI, rebranded at Google Cloud Next '26), the Gemini 3.1 model family, regional availability, pricing tiers, and the platform console. Set the scene for the Nimbus Outfitters capstone that runs throughout Google AI 100.
Your First Gemini API Call - Lab Exercises
Hands-on: make your first Gemini call from Python using the unified google-genai SDK pointed at the Gemini Enterprise Agent Platform. Enable APIs, authenticate, and run a text prompt for the Nimbus Outfitters merch desk.
Auth, Projects, and gcloud
Agent-led teaching on GCP identity for AI developers: organization/folder/project hierarchy, IAM bindings vs AWS/Azure, service accounts, Application Default Credentials, Workforce Identity Federation, and the principle of least privilege for the Gemini Enterprise Agent Platform.
Service Accounts & ADC in Practice - Lab Exercises
Hands-on: create an AI Platform service account (roles/aiplatform.user), mint a key (and then impersonate without a key), set GOOGLE_APPLICATION_CREDENTIALS, contrast with user ADC, and recognize the error signatures when auth goes wrong.
Text, Chat, and Streaming with Gemini
Agent-led teaching on text generation modes: single-turn vs multi-turn chat, streaming with generate_content_stream, system instructions, temperature/top_p, token accounting, and when to choose each pattern.
Build the Nimbus Review Summarizer - Lab Exercises
Hands-on: build a Typer CLI that streams Gemini summaries of 20 Nimbus Outfitters product reviews, tracks token costs per model, and compares Flash-Lite vs Flash vs Pro on the same batch.
Multimodal with Gemini (Images, PDFs, Audio)
Agent-led teaching on multimodal inputs: Part.from_uri for GCS assets, Part.from_bytes for inline payloads, supported MIME types, image + text interleaving, and structured JSON output with response_schema.
Build the Nimbus Product-Photo Describer - Lab Exercises
Hands-on: upload 5 product photos to a Cloud Storage bucket, feed their gs:// URIs into Gemini with Part.from_uri, and produce a structured catalog entry per photo using response_schema.
System Instructions, Safety, and Thinking
Agent-led teaching on prompt engineering for brand-safe copy: system_instruction vs in-prompt framing, Gemini safety categories and thresholds, structured output with Pydantic response_schema, and thinking budgets on reasoning-capable models.
Build the Nimbus Policy-Aware Copy Drafter - Lab Exercises
Hands-on: wire tone_of_voice.md + content_policy.md into a system instruction, emit structured CatalogCopy JSON, flip safety thresholds to force a block, and observe thinking-mode token deltas on gemini-3.1-pro-preview.
Capstone Briefing — The Merch-Desk Assistant
Agent-led capstone briefing. Reviews the Nimbus Outfitters story, recaps all six deliverables, introduces the AssistBundle Pydantic schema, walks through acceptance criteria, and previews the build.
Build the Merch-Desk Assistant (Capstone) - Lab Exercises
Hands-on capstone: build nimbus-assist — a CLI that, given a SKU, loads its product photo + spec PDF from Cloud Storage and emits a validated AssistBundle (summary + catalog entry + 3 FAQ items) via Gemini with response_schema.
This course is currently being updated. Check back soon!
This course includes:
- 24/7 AI Instructor Support
- Live Lab Environments
- 6 Hands-on Lessons
- Completion Badge
Earn Your Badge
Complete all lessons to unlock the Gemini Enterprise Agent Platform — Python Developers achievement badge.
Gemini Enterprise Agent Platform — Python Developers
Awarded for completing the Introduction to Gemini Enterprise Agent Platform for Python Developers course. Demonstrates the ability to build production agents on Google Cloud's Gemini Enterprise Agent Platform with Python — agent definitions, tools, multi-turn conversations, grounding, and deployment.
Skills You'll Earn
Complete all lessons in this course to earn this badge