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World Congress 2026 Europe • Jul 9, 2026 • Session details

3 Ways to Rebuild the Data Stack for Agents

Jordan Tigani

The modern data stack is obsolete for AI agents. Discover how ditching rigid UIs for token-optimized CLIs and programmatic pipelines fundamentally transforms autonomous data querying.

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#1 about 3 min

Transitioning from AI co-pilots to AI-native products

The shift from bolting AI onto human interfaces to building for LLMs as primary users.

#2 about 1 min

Overview of MotherDuck and serverless DuckDB infrastructure

MotherDuck runs faster, less expensive cloud queries built on DuckDB for applications.

#3 about 2 min

How LLMs imitate human data discovery behavior

Large language models can now explore datasets and write SQL through trial and error.

#4 about 2 min

Collapsing the modern data stack with AI models

The traditional segmented stack of ingestion and transformation merges into unified vibe-codable tasks.

#5 about 2 min

Replacing complex BI dashboards with live LLM queries

AI models can generate better live embedded visualizations than traditional business intelligence platforms.

#6 about 1 min

Leveraging scale-to-zero hypertenancy for AI agents

Running DuckDB allows every agent instance to independently scale while ensuring seamless cloud scaling.

#7 about 2 min

Why low-code interfaces hinder autonomous AI agents

LLMs perform better when allowed to write Python pipelines instead of using constrained graphical wizards.

#8 about 3 min

Integrating Model Context Protocol for non-technical users

MCP enables instant authentication and cloud application control directly from AI chat systems.

#9 about 3 min

Passing execution context using RAG and markdown guides

Passing exact product constraints and markdown skills reduces redundant parsing steps for inference engines.

#10 about 1 min

Minimizing token usage with the Tune data format

Replacing verbose JSON with Tune formatting drastically decreases parsing tokens and improves system accuracy.

#11 about 2 min

Balancing Model Context Protocol with direct CLI tools

Chaining local command line tools prevents expensive roundtrips and handles raw data better than MCP.

#12 about 2 min

Designing CLI command hierarchies for AI comprehension

Verbose and descriptive command structures allow LLMs to discover capabilities without polluting context windows.

#13 about 2 min

Enabling autonomous agent account signups and provisioning

Structuring deployment commands so agents can provision their own cloud accounts without human intervention.

#14 about 1 min

Constructing multi-step evaluations for agent reliability

Tracking token expenditure and multi-stage intent resolution requires custom built multi-step evaluation harnesses.

#15 about 5 min

End-to-end autonomous data workflow and dashboard demonstration

An LLM independently configures a warehouse, ingests S3 variables, and engineers an interactive dashboard.

#16 about 1 min

Lessons learned in token efficiency and backward compatibility

Redesigning products for agents requires focusing on resource efficiency and embedding changes directly into documentation.

#17 about 1 min

Comparing JSON and Tune formats during audience questions

Structure-agnostic models parse plain Tune format contents more accurately than strictly nested JSON structures.

Matching moments

1:22 min

Leveraging the comprehensive generative artificial intelligence stack

Duan Lightfoot Duan Lightfoot · WWC 2024

1:30 min

Locating AI agents within the broader machine learning landscape

Jörg Neumann Jörg Neumann · WWC 2025

2:04 min

Inspiration and challenges of scaling AI agent communication

diabhey diabhey · WWC 2025

7:07 min

Exploring AI agent usage within the software engineering industry

Chris Heilmann +2 · LIVE

1:32 min

Building autonomous functions with conversational agent frameworks

Modood Alvi · WWC 2025

2:38 min

The evolution toward agentic and literate software programming

Neel Sundaresan Neel Sundaresan +1 · WWC Europe 2026

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