01 · Architecture Story
Snowflake Cortex AI Products
Designed AI product patterns around governed enterprise data: semantic views provide business meaning, Cortex Analyst enables natural-language analytics, Cortex Search supports retrieval over enterprise knowledge, and agents coordinate tools and data for broader workflows.
- Semantic layer for governed business context
- NLQ and analyst experiences over structured data
- Enterprise search / RAG over unstructured knowledge
- Agent patterns combining data, search and tools
02 · Architecture Story
Agentic Engineering & Spec-Driven Development
Applied AI-assisted engineering inside predefined architectural boundaries rather than relying on unconstrained code generation. Specifications, steering rules, hooks, agents and MCP tool integrations create a repeatable path from requirements to implementation.
- Kiro-based implementation patterns with portable concepts
- MCP connections to governed enterprise tools
- Git and Jira integration patterns
- Hooks, agents, specifications and engineering guardrails
03 · Architecture Story
AI Cost & Engineering Efficiency
Designed cost-aware patterns that reduce unnecessary context, tool calls and compute consumption. The same principle extends from developer assistants to Snowflake AI and data workloads: give each workflow the smallest useful context and the right-sized compute.
- Reusable context and focused specifications
- Narrow MCP tools instead of unrestricted context
- Token-aware prompts and workflow boundaries
- Snowflake credit, warehouse and query-efficiency considerations
04 · Architecture Story
Governance, Observability & Production Trust
Enterprise AI is treated as a production system rather than a standalone model. Identity, policy, evaluation, observability and human approval are incorporated according to the risk and action being performed.
- Governed tool access
- Traceability and operational telemetry
- Evaluation and quality controls
- Human approval for sensitive actions