The Analytics Times

Snowflake: Bringing Agentic AI to All Your Data

In nature, snowflakes are single ice crystals or clusters of ice crystals that fall from the atmosphere, each one forming when water vapor in the air turns directly into ice around tiny dust or pollen particles. Every snowflake possesses six-fold symmetry due to the molecular structure of water, yet all snowflakes have their own unique structure due to the different paths they take through the atmosphere. It is a fitting metaphor for the platform that shares their name: Snowflake, the AI Data Cloud, takes the unique, scattered data assets of every enterprise and organizes them into something structured, powerful, and unlike anything else on the market.

This article walks you through how Snowflake is making agentic AI real for companies across every industry, from its personal work agent CoWork to its AI coding agent CoCo, and shows you practical steps to get started.

Key Takeaways

Snowflake unifies enterprise data, analytics, and ai agents on a single governed AI Data Cloud, helping organizations maximize profits and cut costs with production-grade agentic ai.
Snowflake CoWork acts as a personal work agent for business users, turning natural language questions into cited, auditable answers and triggering actions across tools like Gmail, Slack, Jira, and Salesforce.
Snowflake CoCo is a data-native AI coding agent that converts natural language into executable SQL, Python, and pipeline code using Snowflake’s catalog, lineage, and security context.
Enterprises can start quickly with a 30-day trial (up to $400 in free credits) and curated industry solutions that reduce time-to-value.
Concrete case studies from Pfizer, AT&T, Fanatics, VodafoneZiggo, and GLS demonstrate measurable savings, faster insights, and real business outcomes.

What Is Snowflake's AI Data Cloud?

Snowflake is not a generic cloud storage provider or a simple data warehouse. It is a single AI Data Cloud that unifies data, analytics, and agent frameworks in one governed environment, purpose-built for the generative ai and agentic ai era.

At its core, Snowflake centralizes structured and semi-structured data with built-in governance, security, and role-based access control (RBAC). Row-level policies, column masking, and object-level access controls ensure that every query, whether from a human or an ai agent, respects enterprise permissions. The platform integrates across various data types and clouds seamlessly through a single governed page where users access data and tools, so organizations don’t need to move data to external enclaves or maintain fragile pipelines between systems.

Did you know? Just as snowflakes grow branches because tiny protrusions collect water vapor more quickly as they fall, and temperature influences the primary shape while humidity controls the growth rate and complexity of branches, Snowflake the platform grows in capability as your data estate expands. Snowflakes can form different types such as plates, stellar dendrites, and columns. They primarily consist of clear water ice but appear white due to light scattering. Even artificial snow has a different structure compared to natural snowflakes because it freezes too quickly. The platform’s architecture, like its namesake, is designed for elegant complexity at scale.

This architecture is ideal for generative ai and agentic ai because models need access to fresh, accurate, governed enterprise data rather than isolated silos. Snowflake enables real-time insights with over 96% data refresh rates, and VodafoneZiggo improved data refresh rates to over 96% with the platform. Between 2020 and 2026, Snowflake evolved from a cloud data warehouse into a full AI Data Cloud with native support for LLMs, AI functions, semantic views, ML registries, feature stores, and agent frameworks.

This foundation is what enables CoWork and CoCo to operate directly where the data lives, minimizing data movement, reducing compliance risk, and saving time on infrastructure management. VodafoneZiggo cut costs by 50% using Snowflake, and GLS reduced downtime and costs by replacing legacy systems with the platform.

Snowflake CoWork: Personal Work Agent for Your Enterprise Data

Snowflake CoWork is a personal work agent that takes users from context to clarity to action, entirely within Snowflake’s governed perimeter. Previously known as Snowflake Intelligence, CoWork reached general availability in late 2025 and has since expanded its capabilities to serve business teams across every function.

CoWork uses agentic ai to automate multi-step workflows across tools like Gmail, Jira, Slack, and Salesforce via Model Context Protocol (MCP). It doesn’t just answer questions; it can act on your behalf, triggering notifications, scheduling alerts, and running proactive monitoring routines. Its “Deep Research” mode runs multiple agents in parallel over structured and unstructured data, pulling context and producing reports with cited sources.

For business teams and analysts, CoWork handles questions across billions of rows and returns cited, auditable answers rather than opaque summaries. Visualizations are automatically generated when appropriate.

This translates into several value levers:

Faster decision-making cycles (no waiting days for BI report delivery)
Fewer manual data pulls and reduced dashboard backlog
Better ability to maximize profits from each workflow by acting on insights immediately
Stronger alignment between insights and operations

CoWork can also orchestrate other ai agents, including CoCo, for end-to-end processes. A user might ask CoWork to investigate a revenue anomaly, have CoCo generate the underlying pipeline logic, and then CoWork triggers follow-up actions in Salesforce. This kind of automation is the beginning of truly agentic enterprise operations.

Snowflake CoCo: Data-Native AI Coding Agent

Snowflake CoCo is a data-native AI coding agent purpose-built for data engineering, analytics, and ML inside Snowflake. Formerly known as Cortex Code, CoCo was expanded and rebranded at Snowflake Summit 2026 with major new capabilities.

CoCo turns natural language into executable SQL, Python, and pipeline definitions using Snowflake’s catalog, lineage, RBAC, and compute context. Instead of producing generic code snippets that fail in practice, CoCo grounds every output in your enterprise environment, understanding which tables exist, how they relate, who has access, and what compute resources are available. Snowflake supports production-ready Postgres alongside analytics, so CoCo can connect across diverse systems.

CoCo is supported across Mac, Linux, and Windows environments and works within:

A native desktop IDE
CLI for terminal workflows
Snowsight (Snowflake’s browser UI)
Extensions for VS Code, Claude Code, and other editors
SDK, MCP, and Async API for programmatic integration

It can automatically generate and refactor dbt models, orchestrate Airflow or AWS Glue jobs, and connect to systems like Postgres and Spark. For ML workflows, CoCo generates fully executable pipelines from data ingestion through model training, evaluation, and deployment.

CoCo is enterprise-ready with sandboxed runtimes, centralized permission controls, selectable foundation models (including the Claude and GPT family of models), and cost observability so teams can evaluate token usage and manage AI spend.

How Agentic AI and Generative AI Work Together in Snowflake

Generative ai refers to technology like LLMs that create content, whether that is text, code, or summaries. Agentic ai refers to autonomous systems that take goals, plan multi-step strategies, use tools, and execute workflows. Agentic AI can autonomously perform complex tasks without human oversight, and ai agents can learn from experiences and adapt their behavior over time. These two capabilities are complementary, and Snowflake combines both.

Here is how they work together in practice:

Perception: An agent reads enterprise data (tables, documents, streaming signals) from Snowflake.
Reasoning: Using generative AI, the agent interprets the user’s prompt and selects a strategy.
Goal setting: The agent targets specific KPIs or outcomes (e.g., reduce churn by 5%).
Decision-making: It chooses which tools to call, which queries to run, which downstream actions to trigger.
Execution: CoCo generates pipelines and code; CoWork runs actions in connected tools.
Learning: Feedback loops refine future behavior.

Consider a concrete example: a user asks “build a churn prediction model.” CoCo recognizes data sources, proposes feature engineering, generates the ML pipeline, and handles dependencies. Then CoWork orchestrates downstream tasks: generating dashboard insights, scheduling alerts to stakeholders, and connecting to a CRM to send retention offers. The shared context layer in Snowflake (catalog, semantic views, lineage, ML feature store) keeps both agents aligned.

This design helps organizations move from isolated AI demos to production-grade agents that run securely on live operational data.

Industry Solutions: Using Snowflake and AI Agents to Maximize Profits

Snowflake offers tailored industry solutions spanning Financial Services, Healthcare & Life Sciences, Telecom, Public Sector, and Advertising, Media & Entertainment. Each comes with blueprints, partner accelerators, and reference architectures that help companies operationalize agentic ai faster.

The case study metrics speak for themselves:

Company	Industry	Results
Pfizer	Life Sciences	4x faster processing, 19K annual hours saved, 57% TCO reduction
AT&T	Telecom	84% estimated annual cost savings via results caching, sub-second answers for 90% of self-service queries
NYC Health + Hospitals	Healthcare (city hospital system)	Membership updates reduced from five days to five minutes, 100B+ rows of healthcare data
Merkle	Marketing	64% faster development cycle, 20% estimated cost savings
VodafoneZiggo	Telecom	Cut costs by 50% using Snowflake
GLS	Logistics	Reduced downtime and generated faster insights using Snowflake

AI agents on Snowflake can drive revenue in multiple ways. In advertising, agents optimize campaign reach using first-party data and real-time signals. In financial services, AI can analyze live data for predictive analytics in trade decision-making, enabling next-best-action recommendations. AI systems can continuously monitor network traffic for anomalies in telecom. In retail, AI can streamline supply chain management through automation and demand forecasting. These use cases extend from Singapore to global markets.

Snowflake’s industry blueprints and partner-led accelerators reduce time-to-value versus building bespoke stacks from scratch. Better data quality, faster analytics cycles, and autonomous optimization loops powered by agentic ai all contribute to a company’s ability to maximize profits.

Snowflake CoWork in Action: From Signals to Decisions

CoWork turns raw behavioral signals into automated, repeatable workflows that decision-makers can trust. The process starts with data and ends with action, all within Snowflake’s governed perimeter.

Consider the scale involved in a real enterprise environment. Fanatics manages over 100 million customers, each with hundreds of attributes, producing over 2 billion daily signals. Using CoWork, they stitch together these signals into a unified view (FanGraph), enabling personalized journeys, segmentation, cross-sell campaigns, and addressable audiences built entirely on first-party data.

CoWork handles large, complex datasets across structured and unstructured sources. Business users can:

Query datasets directly using natural language
Segment audiences and trigger cross-sell campaigns without writing code
Build addressable audiences for personalized delivery
Capture successful workflows as reusable “Skills” (playbooks) to standardize best practices across teams

On the governance and cost side, CoWork inherits Snowflake’s existing access controls. Admins can manage budgets for AI credits, set permissions, and monitor usage. Only authorized users and agents can access sensitive data fields. This is critical for excellence in regulated industries like insurance or healthcare, where data access must be tightly controlled.

Automations and scheduled Briefs (alerts) allow recurring tasks to run without manual intervention, freeing teams to focus on strategy rather than data wrangling.

Snowflake CoCo in Action: Accelerating the Data Lifecycle

The enterprise data lifecycle includes five phases: ingest, transform, model, analyze, and operationalize. CoCo accelerates each one.

Discover and ingest: CoCo uses Snowflake’s catalog and semantic catalog search to find relevant datasets. It can generate pipelines that import data from sources like Postgres, Spark, and AWS Glue. Snowflake’s Datastream service (launched at Summit 2026) enables real-time streaming from Apache Kafka-compatible sources.

Transform and model: CoCo generates dbt models, refactors existing ones, handles feature engineering for ML, and proposes join and transform logic based on lineage. Semantic views map business concepts to technical schemas.

Analyze and build ML models: CoCo generates full ML pipelines, including training, evaluation metrics, and model versioning. For example, an organization might ask: “Generate a pipeline to predict churn next quarter.” CoCo would select features, prepare data, train the model, evaluate performance, register the model in Snowflake’s ML registry, and produce downstream reporting. Snowflake reduces downtime and generates faster predictive insights throughout this process.

Forecast demand: Similarly, a retail company could ask CoCo to build a demand forecasting pipeline. CoCo identifies relevant sales data, creates time-series features, trains a forecasting model, and integrates results into inventory management workflows.

Developers stay in their preferred tools throughout. Whether you prefer VS Code, the terminal CLI, or Snowsight, CoCo handles the boilerplate while you maintain control. This increases velocity and reduces errors, especially for research-heavy development tasks.

Snowflake Intelligence Launch Partners and Ecosystem

Snowflake Intelligence is the umbrella for AI and agentic capabilities on the platform, supported by a broad ecosystem of launch partners that serve enterprises across the world’s industries.

The ecosystem includes three categories:

Technology and data providers (e.g., Fivetran, Dataiku) that integrate natively with Snowflake, enriching AI agents with connectors, model libraries, and domain-specific tools
Consulting and SI partners (e.g., Accenture) that help enterprises design strategies, build custom agent stacks, and migrate workloads to Snowflake’s AI Data Cloud
Data exchange partners that provide third-party datasets for enrichment and association with first-party data

Snowflake’s ecosystem maximizes value from all data and applications. Early customers at scale include Fanatics, WHOOP, Shelter Mutual Insurance, AT&T, and Thomson Reuters, all using CoCo and CoWork to streamline pipelines, build agents, and transform customer experience.

This ecosystem enables rapid experimentation. Instead of building every data connector or model in-house, customers can plug in partner solutions and leverage domain expertise in areas like healthcare analytics, marketing measurement, and financial risk modeling.

Security, Governance, and Cost Control for AI Agents

Safe, governed deployment is critical when running AI agents against sensitive enterprise data. A secure environment is non-negotiable, and Snowflake treats governance as foundational rather than an afterthought.

Snowflake supports always-on, unified security and governance. Its RBAC, data masking, and object-level access control ensure that agents like CoWork and CoCo see only what each user is allowed to see. Every agent operation runs under the user’s role, and all inferencing stays inside the Snowflake perimeter with respect to data residency and compliance.

For observability, Snowflake provides:

Prompt and response logging
Query tagging and usage metrics dashboards
Cost tracking per model, per token (input vs. output)
ML lineage tracking across datasets, feature views, model versions, and deployed services

Organizations can select cost-effective generative AI models for each use case. Token-based pricing is transparent; per-model costs are published, covering models like Claude Opus 4.7, Claude Sonnet 4.7, and GPT-5.4. Teams can route lower-stakes tasks to cheaper models without rewriting pipelines, maintaining a fair balance between quality and investment.

Snowflake supports always-on business continuity for demanding workloads. Governance features help organizations comply with regulations across regions and industries (GDPR, HIPAA, etc.) while still innovating with generative and agentic ai. This matters in every industry, from insurance to healthcare to financial services.

How to Get Started: Trials, Credits, and First Projects

You can start with a 30-day Snowflake trial that includes free credits for the AI Data Cloud and agent features. Introductory offers range from $40 in free credits for CoCo to $400 for CoWork and broader platform access, depending on the promotion.

Here is a simple plan for your first project:

Connect a core data source (e.g., sales data, customer behavior data) to Snowflake
Enable CoWork to ask natural language queries over that data
Use CoCo to generate your first analytics pipeline or ML model
Set up semantic views to map business terms to technical schemas
Configure permissions and data masking to protect sensitive fields

Pick a narrow, high-impact workflow as your initial AI agent use case. Weekly revenue reporting, lead routing, or churn risk scoring are all strong candidates. These focused projects create measurable outcomes quickly, making it easier to justify further investment and scale across the organization.

Snowflake documentation, quickstarts, and solution blueprints can guide teams from proof-of-concept to production deployment. Industry-specific templates further shorten implementation timelines.

Best Practices to Maximize ROI and Profits with AI Agents on Snowflake

Connecting agentic ai deployments to measurable ROI means tracking revenue uplift, cost reduction, and productivity gains from the start.

Here are the practices that separate successful deployments from sidecar experiments:

Start with clear business objectives. Define what you want to move (e.g., increase conversion by X%, reduce data prep time by Y%) before selecting agents or models. Don’t build tools first; define outcomes first.
Build cross-functional teams. Combine data engineering, operations, finance, and domain expertise. Have data stewards manage semantic view definitions, feature definitions, and catalog upkeep to create value from day one.
Implement feedback loops. In early stages, have humans review agent outputs. Sample audits and error correction build confidence. Progressively increase automation as reliability improves.
Balance automation with interpretability. Ensure agents’ reasoning is visible. Monitor ML model drift. Version-control pipelines. Evaluate results regularly.
Integrate agents into core processes. Long-term profit maximization comes from embedding agents into the customer lifecycle, supply chain, and finance operations, not running them as isolated experiments.
Monitor costs aggressively. Use Snowflake’s cost dashboards to track token usage and model latency. Choose appropriate models for each use case to avoid runaway AI spend and generate real savings.

Future of Agentic AI on Snowflake

Snowflake is evolving from static analytics to fully agentic data applications. The trajectory is clear: from dashboards you read, to agents that act on your behalf, operating at the speed and scale of global enterprise data.

Emerging patterns include multi-agent systems where planning, experimentation, and optimization agents collaborate on complex workflows. Horizon Context and Horizon Catalog hint at richer contextual layers that help agents discover data, schemas, and semantics across clouds and accounts. Computer vision capabilities and domain-specific agents (e.g., clinical diagnostics, financial risk, marketing measurement) will likely expand the platform’s reach into new development areas.

Snowflake’s model-agnostic approach protects customers from vendor lock-in. As generative AI models continue to innovate, Snowflake customers can swap or add models without rewriting their agent logic. Upcoming capabilities likely include deeper orchestration, richer tool ecosystems, more autonomous data operations, and agents that detect anomalies and fix pipelines without human intervention.

Businesses that align their data strategy with Snowflake’s AI Data Cloud today will be better positioned for agentic AI advances through 2026 and beyond. The organizations that centralize their data, define semantic views, build catalogs, and establish governance now are the ones that will move fastest as these capabilities mature.

The companies that treat agentic AI as a core part of their data strategy, rather than a sidecar experiment, will be the ones that consistently deliver faster, more profitable outcomes.

Start your 30-day Snowflake trial, connect your first data source, and see what CoWork and CoCo can do for your bottom line.

FAQ

How is agentic AI on Snowflake different from using a standalone chatbot?

Snowflake’s agents (CoWork, CoCo, and custom agents) run directly on governed enterprise data with full visibility into lineage, permissions, and compute. Unlike generic chatbots that rely on copy-pasted snippets or disconnected APIs, Snowflake agents can take actions like updating tables, launching pipelines, and notifying stakeholders while respecting role-based access. The operational benefits include reproducible workflows, auditability, and tight integration with existing analytics and BI stacks, which standalone chatbots simply cannot match.

Can I bring my own generative AI models to Snowflake?

Yes. Snowflake supports multiple leading models out of the box (including Claude and GPT families) and is designed to be model-flexible. Enterprises can connect to external model endpoints or deploy their own models, then expose them to agents via standardized interfaces. Governance and cost controls apply regardless of which model is used, helping organizations manage AI spend without sacrificing the ability to innovate with new model releases.

What skills does my team need to start using CoWork and CoCo?

Business users can be productive with CoWork using only natural language and a basic understanding of their data domains. Data engineers and analysts benefit from familiarity with SQL, Python, and tools like dbt or Airflow, but CoCo reduces the amount of boilerplate they must write. Teams should invest in basic prompt engineering, Snowflake platform fundamentals (catalog, lineage, semantic views), and governance practices to get the most from agentic ai on the platform.

How quickly can an enterprise see value from AI agents on Snowflake?

Many organizations can stand up an initial proof-of-concept in a few weeks by focusing on a single high-value workflow such as automated reporting, customer segmentation, or account management. Time-to-value depends heavily on data readiness: cleaner, centralized data in Snowflake leads to faster and more accurate agent behavior. Using Snowflake’s industry templates and partner accelerators can shorten implementation timelines significantly.

What are the main risks of deploying agentic AI on production data, and how does Snowflake address them?

Key risks include unauthorized data access, incorrect agent actions, runaway costs, and compliance violations. Snowflake addresses these through RBAC, fine-grained permissions, data masking, and sandboxed runtimes that reduce the chance of agents seeing or changing what they should not. Observability dashboards, approval workflows, and staged rollouts (dev, test, prod) are essential best practices. Organizations should start with human-in-the-loop oversight and progressively increase agent autonomy as confidence in outputs grows.