22-25 April 2026 
Stop Paying Twice: Agentic AI with DBs, but 40% cheaper
Proposed session for SQLBits 2026TL; DR
Build reliable, cost-efficient AI agents for real data platforms. Learn how idempotent agents eliminate redundant LLM calls, stabilize outputs, and cut token costs by up to 50 percent, with a live demo and plug-and-play code.
Session Details
Agentic AI promises speed and autonomy, but in real enterprise systems it often delivers inconsistency, runaway token costs, and unpredictable outcomes. This session cuts through the noise and tackles a problem few teams are addressing head-on: unreliable agent behavior that silently drains budgets and erodes trust.
You will learn how idempotent AI agents can fundamentally change how agentic workflows operate. By enforcing consistency and predictability, idempotent agents eliminate redundant LLM calls, stabilize responses, and dramatically reduce costs without sacrificing quality. We will introduce i-Check, a patented framework that has already delivered over 90 percent response stability, 40 percent token savings, and 37 percent cost reduction across more than 1,000 real-world enterprise workflows.
This is not a theoretical discussion. You will see a live demonstration showing how i-Check plugs into an existing agent pipeline in under five minutes using a simple Python library. From the second request onward, token usage drops sharply while answer quality improves. You will leave with a clear mental model, practical implementation guidance, and production-ready code you can apply immediately across RAG pipelines, Copilot-style assistants, and complex multi-agent systems.
If you are building AI-native data platforms and care about reliability, performance, and cost control at scale, this session will give you tools you can use today. Check out our white paper (https://drive.google.com/file/d/1tnIqY8k9EETBYnK8ovPjSDVC24FItgC2/view?usp=sharing) and open-source implementation (https://github.com/knowledge-verse-ai/I-Check) to dive deeper into our innovation too!
Key takeaways:
• Understand the concept of idempotent AI and why it is critical for reliable agentic systems
• Learn how to cut LLM token costs by up to 50 percent without degrading output quality
• See a live integration of i-Check with measurable, real-time impact
• Leave with plug-and-play code ready for enterprise agent workflows
You will learn how idempotent AI agents can fundamentally change how agentic workflows operate. By enforcing consistency and predictability, idempotent agents eliminate redundant LLM calls, stabilize responses, and dramatically reduce costs without sacrificing quality. We will introduce i-Check, a patented framework that has already delivered over 90 percent response stability, 40 percent token savings, and 37 percent cost reduction across more than 1,000 real-world enterprise workflows.
This is not a theoretical discussion. You will see a live demonstration showing how i-Check plugs into an existing agent pipeline in under five minutes using a simple Python library. From the second request onward, token usage drops sharply while answer quality improves. You will leave with a clear mental model, practical implementation guidance, and production-ready code you can apply immediately across RAG pipelines, Copilot-style assistants, and complex multi-agent systems.
If you are building AI-native data platforms and care about reliability, performance, and cost control at scale, this session will give you tools you can use today. Check out our white paper (https://drive.google.com/file/d/1tnIqY8k9EETBYnK8ovPjSDVC24FItgC2/view?usp=sharing) and open-source implementation (https://github.com/knowledge-verse-ai/I-Check) to dive deeper into our innovation too!
Key takeaways:
• Understand the concept of idempotent AI and why it is critical for reliable agentic systems
• Learn how to cut LLM token costs by up to 50 percent without degrading output quality
• See a live integration of i-Check with measurable, real-time impact
• Leave with plug-and-play code ready for enterprise agent workflows
3 things you'll get out of this session
• A practical framework to make AI agents predictable, stable, and trustworthy in production
• Proven techniques to significantly reduce LLM token usage and enterprise AI costs
• Hands-on insight into deploying a production-ready solution that integrates in minutes and delivers immediate impact