About this episode

The episode discusses how AI can analyze enterprise data without moving it, featuring insights from AWS and Teradata.

Curious how AI can query your enterprise data without moving it or making things up? AWS and Teradata break down a trustworthy analyst agent built for real production use. Topics Include: Neha Wadhera (AWS) introduces Trinath Yarlagadda and the Teradata Analyst Agent Enterprise AI data prep is costly, stalling most orgs at experimentation Agent answers plain-English questions via traceable SQL, zero data movement Barrier removal drives 3.7x ROI and 40% productivity gains Healthcare demo setup: hospital COPD readmissions, ~$10K cost per incident Four design principles: traceability, no data movement, deterministic-first, governance as code Main orchestrator agent plans, writes SQL, calls Teradata MCP server Complex questions escalate to a context-isolated data scientist agent Built on Claude Agent SDK, running Bedrock Claude Sonnet/Haiku/Opus Live demo: COPD readmission rates explored through iterative agent reasoning Delegation demo: data scientist agent runs in-database analysis, surfaces factors Pre/post tool hooks log every step and cost to CloudWatch Agent hosted on Amazon Bedrock AgentCore, fully serverless and scalable AgentCore delivers runtime, memory, identity, and…

People in this episode

Host: Neha Wadhera

Guest: Trinath Yarlagadda

Topics covered

• AI analytics
• data movement
• enterprise data
• productivity gains
• healthcare
• SQL querying

Keywords

• AI
• analytics
• data preparation
• SQL
• productivity
• healthcare
• AgentCore
• Teradata

Mentioned in this episode

Organizations: AWS, Teradata

Products: Amazon Bedrock, AgentCore, Claude Agent SDK

Places: hospital

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