Enterprise Agentic AI Architecture
Designing enterprise AI experiences that connect governed data, semantic intelligence, retrieval and controlled actions—without turning the model into an unrestricted gateway to business systems.
Agent and tool architecture · Semantic access · Evaluation · Runtime governance
This architecture story focuses on the AI application and agent control plane: how questions are interpreted, how enterprise tools are selected, and how evidence, authorization and human approval shape the response. It is intentionally different from the governed data platform case study, which concentrates on ingestion, identity and the trusted data foundation.
Architecture principles
- Use semantic definitions for business metrics rather than asking an LLM to invent SQL meaning.
- Separate read-only retrieval and analytics from operational actions that modify enterprise state.
- Treat tool identity, authorization, logging and evaluation as architecture—not prompt wording.
From governed data to AI products
The source material describes patterns using Snowflake Cortex AI, Semantic Views, Cortex Analyst and Cortex Search. These serve different purposes: structured questions benefit from governed business semantics, while enterprise documents require search and retrieval. An agent can coordinate both, but the business meaning and access decisions must remain outside the model.
- Semantic layer: consistent metric definitions and permitted analytical dimensions.
- Natural-language analytics: question-to-query experiences anchored to approved semantic models.
- Retrieval: indexed enterprise knowledge with source context for answers.
- Agent coordination: select the smallest authorized tool set for the user’s task.
Question, context and user identity
Analytics, retrieval or action
Semantic query, search or API
Evidence, citations and audit
Agentic engineering and spec-driven development
The original project page identifies Kiro-based, specification-driven development, steering documents, hooks and MCP integrations. The engineering goal is repeatability: describe expected behavior, constrain tool access, and validate generated artifacts before they are promoted.
- Define acceptance criteria, architecture boundaries and non-functional requirements in specifications.
- Use narrow MCP tool interfaces instead of broad, unrestricted access to internal systems.
- Keep Git and Jira integrations within established engineering permissions and review workflows.
- Apply tests, security checks and human review to AI-generated code and configuration.
Design boundary: AI-assisted code generation is an engineering productivity practice; it is not evidence that generated changes are safe without validation.
Cost-aware AI architecture
The source emphasizes focused context, narrow tool usage and compute efficiency. These choices influence both application latency and operating cost. Cost controls belong at the workflow level, not only in the model configuration.
- Limit retrieval to relevant, authorized context rather than forwarding entire documents.
- Avoid repeated agent loops and unnecessary model or tool invocations.
- Select appropriate model capability for each step and instrument usage per workflow.
- Track Snowflake warehouse and query efficiency alongside external model consumption.
Task type and budget
Minimum useful context
Bounded calls and retries
Latency, usage and quality
Security, evaluation and production trust
A production-ready agent needs independent controls for authentication, authorization, tool execution, evidence and operational review. The model can propose an action, but a governed system must decide whether the action is permitted.
- Identity-aware access to semantic models, documents and tool APIs.
- Read-versus-act separation, with approval for sensitive write operations.
- Tracing across model calls, tool results and downstream system activity.
- Evaluation for factual grounding, unsupported claims, task completion, latency and human overrides.
Architecture trade-off: adding agents increases flexibility but also expands the evaluation and operational surface. Use deterministic workflows when they can satisfy the requirement.