Agentic AI in Action — Part 24 - Building a Fraud Ops Escalation Agent with Snowflake CoWork

2026-07-07 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Intermediate, long

Summary

The article details building a fraud operations escalation agent using Snowflake CoWork, a platform rebranded from Snowflake Intelligence at Summit 2026 with expanded capabilities like Deep Research and MCP connectors. It outlines creating a synthetic dataset of 500 customers, 80 merchants, and 20,000 transactions over two months, with approximately 6% flagged. The core process involves configuring a Semantic View to provide governed metrics and adding a custom stored procedure for write actions. The agent is built in Snowsight, integrating these tools and incorporating orchestration instructions to ensure human-in-the-loop approval. This setup allows the agent to answer complex natural language queries about fraud data and proactively suggest escalating high-concern transactions, demonstrating a full, auditable loop from question to governed write action.

Key takeaway

For AI Engineers building operational agents, you should design solutions that combine governed data access with explicit write actions and human approval. This approach, exemplified by Snowflake CoWork's Semantic Views and custom tools, ensures auditable, controlled automation. Implement orchestration instructions to embed human-in-the-loop decision points, transforming natural language queries into traceable, approved actions across various enterprise domains.

Key insights

Snowflake CoWork agents can integrate governed data and write actions for human-approved, auditable operational escalations.

Principles

• Semantic Views ensure consistent agent metrics.
• Tool descriptions guide agent reasoning and actions.
• Human-in-the-loop prevents silent agent actions.

Method

Build a CoWork agent by integrating a Semantic View for data understanding and a custom stored procedure for write actions, then add orchestration instructions for human approval.

In practice

• Use Semantic Views for consistent data interpretation.
• Implement custom tools for agent write actions.
• Configure orchestration for human approval gates.

Topics

• Snowflake CoWork
• Agentic AI
• Fraud Detection
• Semantic Views
• Data Governance
• Human-in-the-Loop

Code references

• Krishsriniv/snowflake-cowork-fraud-ops

Best for: AI Engineer, Software Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.