“Why did our new product underperform last quarter?”
For a retail operations VP, this question is key to driving decisions across marketing, supply chain, and customer experience. Answering it requires connecting sales data, customer feedback, inventory reports, and campaign performance quickly and in a way that the business can act on confidently.
Enterprises today have access to petabytes of data, connected to increasingly sophisticated AI models. However, unlocking real, actionable intelligence remains elusive. Over the years, we at Tiger Analytics have observed that while advanced AI systems are powerful, they often operate in isolation from the enterprise data foundation, the layer that gives insights context, trust and governance. The result is a familiar paradox:
- Data is abundant, but insights are fragmented.
- Models are advanced, but reasoning is disconnected from business context.
- Governance frameworks exist, but models often act without awareness of policies.
Based on our experience helping organizations build AI-driven decision engines, it is clear that the next frontier is agentic intelligence: AI that can ask, reason, and act directly where their data lives. Bridging the gap from siloed AI tools to integrated intelligence platforms is how businesses can deliver meaningful impact. In this blog, we explore how platforms like Snowflake Intelligence can address this challenge, helping organizations move from Data Cloud to Intelligence Cloud.
Caption: A screengrab of the Snowflake Intelligence platform.
What is Snowflake Intelligence?
Snowflake Intelligence is a conversational, agentic AI system built natively within Snowflake’s secure environment. It allows teams to ‘talk to their data’, asking questions in natural language across both structured and unstructured data sources such as tables, CSVs, emails, support tickets, or documents. Its agents can reason, summarize, analyze failures, generate SQL queries, and trigger actions, all while operating within governed boundaries. Agentic AI needs to reason where data resides, securely and contextually. Snowflake Intelligence exemplifies this approach by embedding reasoning capabilities directly within the data environment.
Its key components include:
- Cortex Analyst: A highly accurate and context-aware Natural Language–to–SQL engine. It understands data semantics and user intent beyond keyword mapping, allowing users to query complex datasets as naturally as they would when speaking to an analyst.
- Cortex Search: Powers unstructured data discovery in a Retrieval-Augmented Generation (RAG) architecture. While it currently focuses on text and document data, it is soon to be extended to charts, images, and infographics, a major step toward true multimodal reasoning.
- Cortex AISQL: Together, these functions enable indirect multimodal analysis even today, allowing developers to process structured data, extract unstructured signals, and generate contextual intelligence in a single SQL pipeline.
- Custom Agents: Beyond querying and analysis, custom agents can integrate, orchestrate, and trigger actions. They can connect to other APIs, automate workflows, and even invoke code execution – all through natural language interaction.
- Language Localization: With built-in multilingual support, Snowflake Intelligence enables insights in local languages, democratizing data access across global enterprises.
- Knowledge Extensions: Correlate contextual knowledge from sources like The Associated Press or Stack Overflow.
The Five Technical Pillars that Make Up Snowflake Intelligence
Snowflake’s agentic AI capabilities are built on five enterprise-grade pillars for trustworthy, explainable, and actionable intelligence:
Here’s how the agentic orchestration layer works. For example, an enterprise analyst asks, “Why did last night’s customer churn spike in Region South?” The road to action looks something like this:
- Ask: Cortex Analyst interprets the query using the company’s schema and domain metadata.
- Reason: Agents collaborate to form hypotheses – “Spike due to failed renewal emails”.
- Retrieve: The AI queries the governed data directly – no extraction, no movement.
- Act: It can generate SQL for validation, update a dashboard, or trigger an alert.
- Explain: It traces reasoning steps and data lineage back to the source.
This ‘Ask → Reason → Act’ loop goes beyond conversational AI, evolving into operational intelligence embedded in the enterprise fabric.
How Snowflake Intelligence Differs from Other Agentic AI platforms
Snowflake Intelligence takes a distinct approach when compared with many agentic AI platforms. Based on our observations and Snowflake documentation, key differentiators include:
In-database AI runtime, schema-aware reasoning, and governed execution enable agentic AI workflows that operate directly within the enterprise data environment, thereby supporting scale, context-aware reasoning, and compliance.
Intelligence in Action: Enterprise Use Cases Across Industries
Here is how leading industries can apply agentic intelligence to optimize operations, from interpreting complex documents and predicting pipeline failures to personalizing customer journeys in real time. Each use case illustrates how the architecture of platforms like Snowflake Intelligence enable these capabilities:
Financial Services
- Automated Document Intelligence: Cortex LLMs and LlamaParse interpret loan agreements, KYC files, and audit reports, enabling teams to query documents conversationally.
- Regulatory Monitoring: Cortex Data Agents scan compliance logs and transaction summaries for policy breaches or anomalies.
- Fraud Pattern Detection: Integrated vector search across structured and semi-structured data improves fraud detection by combining transaction history, user logs, and social signals in context.
Data Engineering & Operations
- Pipeline Root Cause Analysis: Engineers can ask, “Why did last night’s ETL job fail?” and Snowflake Intelligence traces lineage, dependency graphs, and recent data quality metrics,explaining the root cause and suggesting corrective SQL or orchestration steps.
- Predictive Maintenance for Data Pipelines: Cortex Agents monitor data freshness, model drift, and latency metrics to predict failures before they occur.
- Dynamic Resource Optimization: AI-driven recommendations adjust warehouse scaling and caching strategies based on observed workloads.
Customer Experience & Support
- Conversational Analytics: Customer support transcripts and feedback forms are analyzed using Cortex Search to identify recurring complaints and sentiment trends.
- Intelligent Ticket Routing: Agents understand query semantics and route support tickets to the right department using contextual classification models.
- Customer Journey Insights: Combine structured CRM data with chat and voice transcripts to uncover hidden churn drivers and personalize responses.
Energy & Utilities
- Grid Performance Analysis: Real-time sensor data from smart meters is analyzed for voltage fluctuations, outages, and energy theft patterns.
- Asset Health Monitoring: Cortex Agents predict equipment degradation using time-series data from IoT devices, reducing unplanned downtime.
- Sustainability Optimization: AI models forecast carbon footprint impact of operational adjustments and recommend energy-efficient alternatives.
Manufacturing
- Visual Defect Detection: Cortex Visual Intelligence processes production-line imagery to identify surface defects or misalignments in real time.
- Predictive Supply Chain: Snowflake Intelligence integrates sales forecasts, shipment delays, and supplier data to anticipate material shortages.
- Digital Twin Analysis: Agentic workflows simulate and optimize production parameters based on historical performance data.
The Road Ahead – Our Point of View
Platforms like Snowflake Intelligence act as a bridge between data and autonomous action with more features and capabilities in the pipeline to extend agentic AI deeper into the enterprise stack. The next phase of this evolution will hinge on multimodality, observability, and semantic automation. Snowflake’s upcoming releases already point in this direction. Features in the pipeline include the ability to upload and query attachments (.xlsx, .csv, .docx, .pdf), enhancements to charts (style, customization, theming, brand logos), and saving and scheduling charts or chat outputs for auto-refresh. On the extensibility front, capabilities like Agents REST API (Cortex Data Object Agent), MCP integration (invoke agents via MCP, or add MCP tools), and semantic view automations to enrich verified queries with Tableau, Power BI, or dbt reflect the push toward greater interoperability and automation. Governance and observability are evolving in parallel. Additions like traceable agent reasoning, usage analytics, and spend insights across document sets show how platforms are embedding transparency and control into AI operations. Collectively, these updates suggest a near-term future where AI agents function as a trusted, auditable co-pilot within enterprise environments.
At Tiger Analytics, we see Snowflake Intelligence’s deep integration with governed enterprise data, Cortex-powered reasoning, and extensible agent framework as forming the foundation of an emerging “Intelligence Cloud”. The next wave of enterprise AI can be built on top of such frameworks, allowing businesses to ask complex questions in natural language, receive contextual, governed, and trustworthy responses, and trigger real-world actions, automatically and securely.
To help enterprises realize value faster, we are building a suite of accelerators and frameworks on top of Snowflake Intelligence:
- Semantic Model Templates: Pre-trained context graphs per industry
- Agent Libraries: Plug-and-play Cortex agent workflows
- Ingestion Accelerators: SharePoint → S3 → Snowflake AI-ready pipelines
- Semantic Model Generator: Automated tool to scan existing Snowflake schemas and generate semantic model YAML files
- Observability Dashboards: Track AI actions, confidence, cost and lineage
By exposing intelligence capabilities through APIs, enterprises can enable a new generation of real-time, intelligent applications, from self-healing data pipelines to proactive compliance bots and context-aware digital assistants.
