Fifty-five percent of surveyed executives say a lack of business context is the main reason their analytics agents produce substandard insights. That finding comes from MIT Technology Review Insights, in custom research sponsored by Snowflake and published November 2, 2026. A day later, at Expedition 2026, Snowflake answered with product: Business Ontology to ground agents in approved definitions, and Cortex AI Gateway as a front door for policies, spend attribution, and model routing. The pairing is deliberate. Context without guardrails still burns money. Guardrails without context still produce answers nobody trusts.
The same tension shows up in the customer quotes Snowflake put next to the launch. Andy Owens, VP of Data and AI at Kargo, said the company “couldn’t answer was which teams and which agents were driving the spend.” After the gateway, Kargo got a breakdown, hard limits, and routing that keeps routine work off the most expensive models. Emrecan Dogan, chief product officer at Glean, put the other half of the problem in one line: as agents grow more sophisticated, context has to be matched with equally strong governance. This post reads the announcement against the MIT Technology Review Insights numbers, then asks what AI agent governance still requires once a single-platform gateway is in place.
What Snowflake announced at Expedition 2026
Snowflake’s November 3 press release frames two Horizon Catalog capabilities as the foundation for trusted agents. Business Ontology, described as public preview soon, is the place to record what business terms officially mean and to serve only approved definitions to agents. Cortex AI Gateway applies one set of policies to agent connections across clouds, frameworks, and providers. Christian Kleinerman, Snowflake’s EVP of Product, said the enterprises that win “won’t be the ones with the most agents” but the ones whose agents know what the business means, abide by guardrails, and can account for the actions they take.
On the gateway side, Snowflake highlights cost governance that attributes spend to a user, team, department, agent, or app and enforces hard limits; observability that records agent activity in the customer’s own Snowflake account; Dynamic Model Routing that matches requests to organization-approved models by cost, accuracy, and latency; Agent Abuse Detection that analyzes full trajectories; and MCP governance for tool calls. Availability is not uniform. Cost governance and observability are generally available in the footnotes Snowflake published with the release. Dynamic Model Routing, Agent Abuse Detection, and Agent Trace Intelligence are listed as private preview. MCP governance through Cortex AI Gateway Tools is public preview. Business Ontology itself is public preview soon. Leaders evaluating the stack should treat those footnotes as part of the product story, not fine print.
Taken together, the announcement is a bet that AI agent governance starts with shared meaning and a single policy edge for agents that touch Snowflake data. That bet maps cleanly to the problems the sponsored research called out the day before. It also leaves open a measurement question that most enterprises already live with: plenty of agents never route through Snowflake at all.
What the MIT Technology Review Insights numbers add
The MIT Technology Review Insights report “Raising the quality of insights in agentic analytics,” published November 2, 2026, is custom content from Insights, not MIT Technology Review’s editorial staff, and was sponsored by Snowflake. Methodology on the press release: July and August 2026, 400 CDAO, CIO, CTO, CAIO, and other senior data and technology executives at organizations with at least $500 million in annual revenue. The numbers that matter for governance are blunt.
On average, 48% of analytics-agent insights lead to actions that improve the business, up from 37% a year earlier according to respondents. “Insight leaders,” a smaller cohort, say more than 70% of agent insights lead to improving actions. Fifty-five percent of executives cite lack of business context as the main reason for substandard insight quality; 45% cite fragmented or inaccessible data. Fifty-two percent say agents generate too many insights to properly evaluate. Sixty percent are prioritizing stronger AI and data governance, and 45% are adding a semantic layer.
Those priorities explain why Snowflake led with ontology and a gateway rather than another model announcement. A semantic layer and governed definitions attack the 55% context problem. Cost attribution, hard limits, and routing attack the spend side of the same agent sprawl. What the survey does not claim is that platform-native controls alone close the loop on outcomes. Nearly half of agent insights still fail to improve the business on average, and more than half of organizations cannot evaluate the volume of insights their agents produce. That is a measurement gap as much as a model gap, and it is where an AI system of record starts to matter beyond any one data cloud.
The measurement gap a platform gateway does not close
A Snowflake-native gateway can govern agents that live on Snowflake data: attribute their spend, enforce hard limits where those controls are available, route routine work to cheaper models, and keep traces where the customer can query them. That is necessary work for any team running agents against a warehouse at scale. It is not the same as enterprise-wide AI agent governance. Most large organizations also run ChatGPT Enterprise, Microsoft Copilot, Claude, Cursor, n8n workflows, and custom agents on other clouds. Those agents have their own keys, owners, and invoices. A hard limit inside one platform does not see a coding agent that jumps overnight, or a stolen key that burns a Bedrock bill in thirteen hours, the pattern we covered in our LLMjacking analysis.
This is the same unit-of-measure problem finance teams hit when they watch a total AI invoice instead of spend by owner, project, and outcome. In our write-up of Deloitte’s AI cost findings, only a small share of CFOs said their organizations respond to failed AI cost controls in near real time. FinOps for AI fails the same way when attribution stops at the provider boundary. Cost governance that stops at Snowflake is real progress for Snowflake workloads. It still leaves the CFO and CISO stitching together screenshots from every other stack.
Olakai was built for that wider surface. As a vendor-neutral Enterprise AI Intelligence Platform, Olakai sits as a measurement layer across coding, assistive, and agentic tools, attributing spend and usage to people, projects, and providers rather than to a single warehouse edge. Olakai Agentic and the broader complete AI monitoring view exist so leaders can see agents that never touch Snowflake next to the ones that do. Platform gateways and a cross-stack system of record are complementary: one enforces policy where the data lives; the other answers which agents exist, who owns them, what they cost, and whether they return anything.
What leaders should do now
Snowflake’s launch and the Insights survey point to the same operating checklist, whether or not your agents run on Snowflake tomorrow:
- Inventory every production agent and agent-like workflow across stacks, including Copilot, ChatGPT, Claude, Cursor, n8n, custom services, and warehouse-native agents, and assign a named owner to each one.
- Require spend attribution to a user, team, or cost center before an agent goes to production, and turn on hard caps where the platform supports them, the way Cortex AI Gateway’s generally available cost governance is designed to do.
- Treat business definitions as governed assets: if two dashboards disagree on net revenue, an agent will invent a third. A semantic layer or ontology with accountable owners is the fix the 55% context finding is asking for.
- Measure outcomes, not only calls. Track which agent insights lead to actions that improve the business, and cut or retrain agents that generate volume nobody evaluates.
- Separate provider-side enforcement from cross-stack visibility. Quotas and hard limits stop a runaway process. A measurement layer tells you which keys and agents exist when the bill arrives from somewhere else.
For a CFO, the immediate ask is attribution and caps tied to owners, plus an AI ROI view that connects spend to outcomes rather than to call volume. For a CISO, the same inventory is a risk register: unowned agents are shadow AI with a budget. Inside AI spend governance and n8n agent monitoring show how that inventory looks when workflow tools and shared service keys are in scope, not only chatbots.
Where to start
Start with the agents that already touch revenue or customer data and answer four questions for each one: who owns it, which platform it runs on, how spend is attributed today, and what share of its insights led to an improving action in the last quarter. If the answers require a tour of five consoles, you have the problem Snowflake is selling into and the gap a single gateway cannot cover alone. Organizations that put Business Ontology-style definitions and gateway-style limits where their data lives, and keep a vendor-neutral system of record across every other agent stack, will treat the next spend spike as an incident with an owner. Those that only buy the gateway will govern one door and leave the rest of the building unlocked.
Talk to an expert about inventorying agents across stacks, attributing spend to owners, and measuring which insights actually improve the business. Olakai gives finance, security, and AI leaders one Enterprise AI Intelligence Platform view of what agents cost, who runs them, and what they return, with AI governance that extends past any single data cloud.