Why Your Snowflake AI Agent Is Failing

Impressive in the demo. Quietly shut down in production.

Most Conversational AI solutions and AI agents built on Snowflake look impressive in a demo, but struggle when deployed to real business users. Business users expect insights, context, and clear recommendations, but many chatbots simply return a wall of numbers. Users still have to switch back to BI tools for basic charts, and every time additional context is needed, they end up going back to an analyst.

The chatbot that was supposed to eliminate those steps simply adds more steps. Add ongoing maintenance and governance challenges, and organizations quickly end up with an AI solution that is costly to run, hard to trust, and even harder to improve. That is exactly why so many conversational and agentic AI projects are quietly shut down.

A production-ready Conversational AI architecture on Snowflake

In this webinar, OSI Digital will show how we approached these challenges differently. Whether you're planning your first conversational AI initiative, leading a broader data strategy, or trying to improve a chatbot that isn't gaining adoption, you'll leave with a practical blueprint for building a Conversational AI solution your executives and business users can trust.

1. 1
   Why agents fail in productionUnderstand the most common production challenges, including untrusted answers, rising token consumption, unpredictable query costs, weak governance, and poor user adoption.
2. 2
   Cut token usage and cost per queryLearn how intelligent context management, prompt optimization, conversation history controls, and selective LLM usage can reduce unnecessary token consumption and cost per query.
3. 3
   Move from raw numbers to real insightSee how governed semantic models and business definitions help move beyond raw numbers and generate meaningful insights, summaries, trends, and business context.
4. 4
   Keep users out of the BI toolDiscover how smart visualizations, charts, underlying data, and follow-up analysis can be delivered within the same experience, without forcing users to switch back to BI tools.
5. 5
   Build a continuous improvement loopExplore how telemetry, user feedback, response tracking, query history, and error monitoring create a continuous improvement loop for accuracy, usability, governance, and adoption.

Speakers