{"templateName":"base-page-template54","cssClassNames":"page basicpage summit-page","allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"language":"en","description":"Learn how an agentic control plane helps enterprises govern AI agents, enforce policy, manage tool access, and turn context into authorized action.","title":"Agentic Control Plane: Governance for AI Agents | Snowflake","analyticsPageType":"homepage","analyticsCategory":"general","analyticsSubCategory":"","excludeFromAnalytics":false,":mappedPath":"/en/artificial-intelligence/ai-governance/control-plane/",":type":"snowflake-site/components/structure/page",":items":{"root":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"markup_editor_928258845":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-banner":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-header":"aem-GridColumn 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An agentic control plane gives organizations a way to manage how agents access context, use tools, follow policy and act across business systems.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-7052315023",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-7562cb013d","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Read 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Ethics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-fairness/\"\u003EAI Fairness\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EAI Risk Management\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-safety/\"\u003EAI Safety\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003EAI Traceability\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003EAI Transparency\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003EAlgorithmic Bias\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EEU AI Act\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/iso-42001/\"\u003EISO 42001\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/model-card/\"\u003EModel Card\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/model-governance/\"\u003EModel Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/responsible-ai/\"\u003EResponsible AI\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-3a94e60b50","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"callout__0":{"id":"text-3531dc063f","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EAGENTIC CONTROL PLANE DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAn agentic control plane is the governance and coordination layer that helps enterprises manage how AI agents access context, use tools, follow policy and take authorized action across systems.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-d03fbcf9b4","text":"\u003Cp\u003EFor decades, traditional software has been built around predefined workflows. Agentic AI complicates that model by introducing autonomy.\u003C/p\u003E\n\u003Cp\u003EInside permissioned enterprise systems, an \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/\"\u003EAI agent\u003C/a\u003E behaves like an actor with delegated authority. It may read governed data, generate SQL, update a workflow, change a system of record or trigger an action in another application. As a result, the organization must govern not just what an application can do, but the many routes an agent may take across data, tools, and business processes.\u003C/p\u003E\n\u003Cp\u003EIf those controls live scattered across prompts, application settings, service accounts and individual integrations, the organization has no consistent way to see or enforce them.\u003C/p\u003E\n\u003Cp\u003EThis is the role of the agentic control plane: a coordination and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003Egovernance\u003C/a\u003E layer for agentic systems. Snowflake CEO \u003Ca href=\"https://www.snowflake.com/en/blog/agentic-enterprise-control-plane/\"\u003ESridhar Ramaswamy\u003C/a\u003E called the agentic control plane “the missing layer” for the enterprise, one that can “translate intelligence into authorized enterprise action.”\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-an-agent-control-plane":{"id":"title-v2-fcfdc8e28a","additionalClasses":"anchor-title anchor-title--what-is-an-agent-control-plane","type":"heading2","lines":["What is an agent control plane?"],":type":"snowflake-site/components/title-v2"},"text_what-is-an-agent-control-plane_0":{"id":"text-14227d8da5","text":"\u003Cp\u003EAn agentic control plane is the centralized layer that deploys, operates, monitors and governs AI agents across an organization. It provides a shared way to manage agent identity, enforce runtime policy, observe behavior, register versions, supply governed context and control access to tools.\u003C/p\u003E\n\u003Cp\u003EWithout a shared governance layer, agents tend to inherit the limits of the environment where they were built. Each may have its own logs, access model, evaluation method and tool permissions. As adoption spreads, the organization ends up with many agent runtimes and no consistent way to see which agents exist, what data they accessed, the actions they attempted or which policies governed those actions.\u003C/p\u003E\n\u003Cp\u003EAn agentic control plane gives enterprises a way to govern those agents as a connected operating layer: aligning identity, policy, context, tool access, execution and auditability across the agent estate.\u003C/p\u003E\n\u003Cp\u003ERamaswamy explains the importance of the agentic control plane this way: “To effectively harness agentic technology, enterprises need more than models and applications. They need a coordinating layer, a central control plane that aligns intelligence, enterprise data, policy, and execution across the organization to drive agentic cohesion.”\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_what-is-an-agent-control-plane_0":{"id":"quote-item-4b21299034","alignment":"left","layout":"simple","quoteSourceName":"Sridhar Ramaswamy","quoteSourceTitle":"CEO of Snowflake","showQuoteIcon":true,"quote":"To effectively harness agentic technology, enterprises need more than models and applications. They need a coordinating layer, a central control plane that aligns intelligence, enterprise data, policy, and execution across the organization to drive agentic cohesion.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_what-is-an-agent-control-plane_1":{"id":"text-68dbf1c639","text":"\u003Cp\u003EThe term “control plane” comes from system architecture, where one layer manages how work is configured and governed while another layer does the work.\u003C/p\u003E\n\u003Cp\u003EIn distributed systems, the data plane is where work happens: requests are processed, data moves, functions run and applications respond. The control plane governs how that work is configured and managed. For AI agents, the data plane is where each agent reasons, calls tools, retrieves context, writes outputs and completes tasks. The control plane sits above those agent runtimes, applying identity, policy, monitoring and lifecycle controls across them.\u003C/p\u003E\n\u003Cp\u003EThat separation is even more important when organizations start using different models, frameworks and deployment approaches. A vendor-agnostic control plane gives the enterprise a consistent governance layer across those systems, rather than tying policy and auditability to one model vendor or agent framework.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_what-is-an-agent-control-plane_0":{"id":"card-v2-7b526535bd","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","image":{"id":"image","height":"360","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--2067c6af-fcfe-4cc2-91d9-7816a21e8c7d/simon-ai-logo-nav.png?preferwebp=true&quality=85","alt":"Simon AI","lazyEnabled":true,"width":"840",":type":"snowflake-site/components/image"},"type":"content-card","title":{"id":"title","type":"heading4","lines":["Simon AI"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/simon-ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Read the full case study"},"text":{"id":"text","text":"\u003Cp\u003ESimon AI uses Snowflake Cortex AI and Snowpark Container Services to power composable AI agents that help marketers deliver contextual personalization at scale without moving data or compromising governance. Running directly in Snowflake’s AI Data Cloud, Simon’s agents give marketers governed, real-time access to structured and unstructured data, helping customers build contextually relevant audiences 90% faster and identify 100+ new high-value customer cohorts.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_what-an-agent-control-plane-does-core-functions":{"id":"title-v2-09d4b997ed","additionalClasses":"anchor-title anchor-title--what-an-agent-control-plane-does-core-functions","type":"heading2","lines":["What an agent control plane does: core functions"],":type":"snowflake-site/components/title-v2"},"text_what-an-agent-control-plane-does-core-functions_0":{"id":"text-b825521d91","text":"\u003Cp\u003EBecause agents combine reasoning, retrieval and tool use, the control plane has to coordinate what happens before an action is taken: who or what is acting, which context is available, which policies apply, when human judgment is required and how the result is recorded afterward.\u003C/p\u003E\n\u003Ch3\u003EEnforce governance and policy at runtime\u003C/h3\u003E\n\u003Cp\u003EAn agent policy has limited value if it exists only in a design document or prompt template. At runtime, the control plane needs to evaluate whether an agent should retrieve a table, call an external tool, send a message, update a record or escalate to a human reviewer.\u003C/p\u003E\n\u003Cp\u003EThose policies may depend on the user, the agent, the data classification, the tool being called, the requested action and the risk level of the workflow. For example,a customer service agent might be allowed to summarize a case history without approval, but a refund, account change or disclosure of sensitive information requires additional checks. Runtime governance gives the organization a way to constrain agent behavior when the agent moves from analysis into action.\u003C/p\u003E\n\u003Ch3\u003EGive agents a verifiable identity\u003C/h3\u003E\n\u003Cp\u003EAn enterprise agent needs an identity that’s separate from the user, the model and the application hosting it. Without that identity, it’s difficult to answer basic audit questions: Which agent accessed the data? Which user authorized the session? Which service account called the tool? Which policy allowed the action?\u003C/p\u003E\n\u003Cp\u003EAgent identity supports \u003Ca href=\"https://www.cisa.gov/topics/cybersecurity-best-practices/zero-trust\" target=\"_blank\"\u003EZero Trust\u003C/a\u003E approaches for AI systems. Rather than granting standing access to tools or data sets, the organization can require just-in-time access based on the agent’s role, the user’s permissions, the task and the current policy state. A procurement agent, for instance, may have permission to read approved supplier data during a sourcing workflow, but not to access unrelated contract repositories or send purchase orders without approval.\u003C/p\u003E\n\u003Cp\u003EAttestation also depends on identity. When an agent has a verifiable identity, the system can create a record tied to that identity. This is essential for incident review, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-compliance/\"\u003Ecompliance\u003C/a\u003E reporting and ongoing \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/agent-evaluation/\"\u003Eevaluation\u003C/a\u003E.\u003C/p\u003E\n\u003Ch3\u003EStandardize observability across the agent lifecycle\u003C/h3\u003E\n\u003Cp\u003EAgent observability gives teams the traces, metrics and evaluations needed to inspect agent behavior. The control plane consumes that telemetry and applies it across agents, workflows and environments.\u003C/p\u003E\n\u003Cp\u003EFor an agentic workflow, a trace might include retrieval steps, tool calls, intermediate reasoning artifacts, policy checks, approvals, retries, failures and final outputs. Over time, traces show whether the agent is overusing tools, retrieving irrelevant context, escalating too often or producing outputs that fail evaluation.\u003C/p\u003E\n\u003Cp\u003EA control plane standardizes how that information is collected and reviewed. Instead of each agent framework producing a different monitoring view, the enterprise gets a consistent record of behavior across the agent lifecycle: development, testing, deployment, runtime monitoring and retirement.\u003C/p\u003E\n\u003Ch3\u003EManage lifecycle, versions and governed context\u003C/h3\u003E\n\u003Cp\u003EAgent behavior changes over time as tool schemas, model providers, business rules or data sources change around it. Without lifecycle management, teams have a harder time tracing which version of the agent ran, which context it used and whether the controls applied, especially when agents operate across teams.\u003C/p\u003E\n\u003Cp\u003EThe control plane provides a place to register agents, track versions, attach owners and manage deployment status. It also helps govern the context agents use. In many enterprise workflows, the most important input is the governed business context that defines customers, products, accounts, policies, metrics and process state.\u003C/p\u003E\n\u003Cp\u003EA sales agent that answers questions about pipeline health, for example, needs the current definition of qualified pipeline, access to approved account data and visibility into the user’s permissions. When context is supplied through the control plane, agents can work from governed sources rather than improvised copies, stale extracts or user-provided files.\u003C/p\u003E\n\u003Cp\u003ELeo Rodriguez, Principal Product Marketing Manager, AI/ML, at Snowflake, puts the context problem plainly: “The AI models are intelligent. The problem is that they don’t automatically have the business context to be trusted by the business. To get accurate answers, you have to give the model the definitions, relationships, permissions and trusted sources it needs to reason over enterprise data.”\u003C/p\u003E\n\u003Ch3\u003EControl model and tool access through gateways\u003C/h3\u003E\n\u003Cp\u003EAs agents interact with models and tools, gateways become a practical part of the control plane.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EAn LLM gateway manages access to models:\u003C/strong\u003E An LLM gateway may route requests across providers, enforce key and budget controls, apply guardrails, capture usage and standardize logging. For organizations using multiple models, this layer keeps model access from becoming a set of disconnected credentials embedded across applications.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAn MCP gateway manages agent-to-tool access.\u003C/strong\u003E The \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-snowflake-mcp-server/\"\u003EModel Context Protocol\u003C/a\u003E (MCP) gives agents a structured way to connect with tools and external systems, but that connectivity also expands the surface area for policy enforcement. A gateway can broker access to tools, inspect requested actions, apply permissions and record what the agent attempted to do.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ETogether, these gateways help the control plane govern both sides of agent execution: the model calls that produce reasoning and the tool calls that turn reasoning into action.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003EWatch leading AI researcher Andrew Ng explore the rise of AI agents and agentic reasoning:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-an-agent-control-plane-does-core-functions_0":{"id":"embed-4503a723a0","youtubeVideoId":"KrRD7r7y7NY","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_control-plane-vs-orchestration-vs-observability":{"id":"title-v2-619877498d","additionalClasses":"anchor-title anchor-title--control-plane-vs-orchestration-vs-observability","type":"heading2","lines":["Control plane vs. orchestration vs. observability"],":type":"snowflake-site/components/title-v2"},"text_control-plane-vs-orchestration-vs-observability_0":{"id":"text-c7dbf3ed2c","text":"\u003Cp\u003EThe control plane, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/agent-orchestration/\"\u003Eorchestration\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/observability/\"\u003Eobservability\u003C/a\u003E are closely related, but they solve different problems.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EOrchestration coordinates execution:\u003C/b\u003E In an agent workflow, orchestration determines which agents or tools run, in what sequence, with which inputs and under which branching logic. A multi-agent system for customer onboarding might assign one agent to validate account data, another to check compliance requirements and a third to draft a kickoff plan. The orchestration layer manages the sequence and handoffs.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EObservability records what happened:\u003C/b\u003E It captures traces, metrics, evaluations, tool calls and outputs so teams can inspect agent behavior. When a \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERAG\u003C/a\u003E workflow returns an unsupported answer or an agent loops through repeated tool calls, observability gives developers and operators the evidence needed to diagnose the issue.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EThe control plane governs across both layers:\u003C/b\u003E It manages identity, policy, auditability, access and lifecycle controls regardless of which orchestration framework coordinates the workflow or which observability system collects telemetry. In practice, the control plane may use observability data to enforce governance decisions, and it may apply policy to orchestrated workflows before, during and after execution.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-agent-control-planes-matter-for-enterprise-ai":{"id":"title-v2-a07a1f3c6c","additionalClasses":"anchor-title anchor-title--why-agent-control-planes-matter-for-enterprise-ai","type":"heading2","lines":["Why agent control planes matter for enterprise AI"],":type":"snowflake-site/components/title-v2"},"text_why-agent-control-planes-matter-for-enterprise-ai_0":{"id":"text-aa298dc984","text":"\u003Cp\u003EEnterprise AI agents operate in environments where data access, business process and compliance obligations already exist, and they have to respect those conditions.\u003C/p\u003E\n\u003Cp\u003EIf agents are acting inside enterprise systems, they have to be governed like actors in those systems, not like passive interfaces layered on top of them. For example, a human analyst may have permission to view regional sales data but not customer-level financial terms, and a data engineer may be allowed to change a pipeline in development but not deploy it to production without review. When agents begin assisting with those same workflows, the control model has to follow the action.\u003C/p\u003E\n\u003Cp\u003EThe primary risk isn’t that agents will suddenly start going rogue. In many cases, the immediate risk is inconsistency. One agent logs full traces, while another stores only final outputs. One workflow uses role-based permissions, while another stores a broad tool credential. Over time, those differences make it harder to scale agentic AI with confidence.\u003C/p\u003E\n\u003Cp\u003EFor Rodriguez, the control plane is most important when agents move across the boundaries that enterprises have traditionally governed separately: data, models and third-party tools:\u003C/p\u003E\n\u003Cp\u003E“The most underrated control plane capability is bringing data and AI governance together. A lot of companies treat the data control plane and the AI control plane as separate perimeters. But as agents start using enterprise data, models and third-party tools in the same workflow, those controls have to come together.”\u003C/p\u003E\n\u003Cp\u003EA control plane gives organizations a more governable architecture. New agents can be registered with owners and versions. Tool access can be mediated rather than embedded. Policies can be applied at runtime instead of copied into prompts. Audit records can follow agent activity across systems. When an incident or compliance question arises, the enterprise has a path to reconstruct what happened.\u003C/p\u003E\n\u003Cp\u003EFor regulated industries, the record may become as important as the output itself. Before a regulated organization can benefit from what AI agents can do, they need evidence of context, authorization, policy enforcement and human review where required.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-agent-control-planes-matter-for-enterprise-ai_0":{"id":"text-5eefe213f9","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EA common mistake is treating the control plane as another observability dashboard. Observability shows what happened, but the control plane helps determine what agents are allowed to do before, during and after execution.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_snowflake-the-control-plane-for-the-agentic-enterprise":{"id":"title-v2-1479fba9ea","additionalClasses":"anchor-title anchor-title--snowflake-the-control-plane-for-the-agentic-enterprise","type":"heading2","lines":["Snowflake: the control plane for the agentic enterprise"],":type":"snowflake-site/components/title-v2"},"text_snowflake-the-control-plane-for-the-agentic-enterprise_0":{"id":"text-2536f49b62","text":"\u003Cp\u003EThe control plane belongs close to \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Egoverned data\u003C/a\u003E, context and policy. The next phase of enterprise AI depends on connecting intelligence to trusted enterprise data and translating that intelligence into multi-step action inside the systems where work happens.\u003C/p\u003E\n\u003Cp\u003EMany enterprises already have access controls, policies, lineage and shared context attached to the data foundation. As agents begin to act on business information, the control plane has to preserve those controls rather than route sensitive context through ungoverned movement or disconnected application layers.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://ai.snowflake.com/\"\u003ESnowflake CoWork\u003C/a\u003E provides a control-plane foundation for business users and knowledge workers through governed question answering, multi-step work and tool-based action. \u003Ca href=\"https://www.snowflake.com/en/product/snowflake-coco/\"\u003ESnowflake CoCo\u003C/a\u003E extends the same foundation to builders as a data-native AI \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/coding-agents/\"\u003Ecoding agent\u003C/a\u003E for data engineering, analytics and AI workflows. For developers, data engineers and AI teams, it means agentic assistance can operate with awareness of Snowflake data, metadata, account context and governance rather than sitting outside the environment where governed work already happens.\u003C/p\u003E\n\u003Cp\u003EAs agents connect to external tools and systems, governance has to extend beyond prompts and data permissions into the actions agents attempt to take. MCP gateways and similar control points can broker tool access, enforce permissions and record what the agent attempted to do.\u003C/p\u003E\n\u003Cp\u003EEnterprise AI will only scale if agents can be treated as governed participants in business processes. 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An agent may begin with a user request, but the work can quickly move through governed data, tool calls, workflow changes and business processes where permissions and auditability already matter.\u003C/p\u003E\n\u003Cp\u003EThe control plane gives organizations a way to coordinate AI agent activity through the same foundation that governs data, context and action. Identity, policy, observability and lifecycle controls become part of how agentic work runs, rather than separate checks recreated for each agent experience.\u003C/p\u003E\n\u003Cp\u003EThat foundation becomes even more important as agents multiply. 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