Google Cloud Solution Agentic AI Data Science Workflow by @google

Legacy Name	Updated Name
Vertex AI	Gemini Enterprise Agent Platform
Vertex AI Agent Engine	Gemini Enterprise Agent Runtime

Google Cloud Solution Agentic AI Data Science Workflow by @google · Skillet

Step 1: Retrieve relevant Google Cloud documentation: Use available search or fetch tools to read the content of the following Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase before proceeding.
Step 2: Define agentic AI design pattern: Select the appropriate agent design pattern and agent breakdown based on the workload requirements:
- Recommended primary pattern: Coordinator pattern.
- Alternative patterns:
  - Single-agent pattern: For simpler workloads scoped to a single data source and direct tool use without multi-agent orchestration overhead.
  - Sequential or parallel pattern: For deterministic data processing pipelines with predefined, non-adaptive execution steps or concurrent data gathering.
  - Review and critique pattern: For complex or high-stakes data science tasks that require dedicated critic loops.
Step 3: Map components to Google Cloud products: For each component in the confirmed technical decomposition and agentic design pattern, identify the appropriate Google Cloud products and features, based on the guidelines in /references/product-mapping.md.
Step 4: Create architecture diagram: Create an architecture diagram that shows the components, their relationships, and data/control flows.
- The diagram must be in the Mermaid format: https://github.com/mermaid-js/mermaid.
- The diagram must use component labels and groupings consistent with the official Google Cloud architecture icons.
Step 5: Generate design recommendations: Generate design guidance based on the guidelines in /references/design-recommendations.md.
Step 6: Draft solution architecture: Compile the requirements, technical decomposition, product mapping, architecture diagram, and design recommendations into a single Markdown file named solution-architecture-guide.md, based on the template in /assets/output-template.md.
Step 7: Request review: Present the generated solution architecture to the user and request their feedback or approval. You must halt and wait for the user's explicit approval before proceeding to Phase 3.
Step 8: Iterate: If the user requests changes, then generate an updated solution architecture and repeat steps 2-7 in this phase until the user explicitly approves the solution architecture.

Google Cloud Solution Agentic AI Data Science Workflow

Data science workflow with AI agents solution

Workflow

Product Renaming & Terminology

Phase 1: Requirements discovery and analysis

Phase 2: Solution design

Phase 3: Implementation plan

Phase 4: Solution validation