In 2026, Snowflake Cortex has evolved from simple LLM functions to a robust platform for autonomous agentic workflows. By leveraging Cortex Agents, organizations can now build systems that not only query data but also plan and execute multi-step reasoning tasks.

Core Components of Autonomous Workflows

• Cortex Analyst & Search: These services provide agents with secure access to structured metrics and unstructured documents.
• Semantic Models: These act as “translators,” providing the high-level business logic and rules that ensure agents interpret raw data correctly.
• Custom Tools: Developers can wrap Python Stored Procedures or UDFs as tools, allowing agents to interact with external APIs or perform complex calculations.
• Orchestration Instructions: These guide an agent’s reasoning process, defining how it should break down questions and which tools it should prioritize.

---

Workflow Orchestration Model

Layer	Function	Technology
Reasoning	Goal-based planning and reflection	Cortex Agents (LLM-based)
Data Access	Context-aware retrieval	Cortex Search & Analyst
Execution	Action-oriented tasks	Stored Procedures / Custom Tools
Governance	Permission and audit logs	Snowflake RBAC & Model Context Protocol

By integrating the Model Context Protocol (MCP), these agents now bridge the gap between Snowflake and external ecosystems like Salesforce or GitHub, creating truly end-to-end autonomous loops.