How Do Data Systems Evolve in an Agentic World?
A community exploring how modern data stacks evolve into agentic data stacks — enabling AI-powered data transformation, autonomous data engineering with data agents, and intelligent data systems powered by data engineering agents.
Vendor-neutral · Practice-driven · Architecture-first
Latest from the community
Agentic Data Stack Evaluation FAQ: Practical Answers for Platform Teams
Common questions about evaluating agent-ready data stacks — from measuring readiness quantitatively to making existing tools agent-friendly.
Agentic Data Stack Weekly #1
Inaugural issue: MCP protocol gains traction in data tools, Apache Iceberg adoption accelerates, and why semantic layers are the missing piece for data agents.
Across 8 categories — from catalog services to data agents. The living wiki of the agentic data stack ecosystem.
The Agentic Data Stack
A reference architecture for intelligent data systems designed for agents — enabling autonomous data pipelines, context-aware operations, AI-powered reasoning, and governed execution.
What we focus on
Modern data stack optimized for humans. Agentic data stack optimized for data agents, autonomous data pipelines, and AI-powered analytics driven by data engineering agents.
Autonomous Data Engineering
Data engineering agents autonomously build models, pipelines, dashboards, and analytics from raw data — then continuously optimize, monitor, and maintain them with intelligent automation.
AI Agent Data Architecture
Build intelligent data systems designed for AI agents. Agentic data catalogs, semantic layers, lakehouse engines, and autonomous data pipelines — architected for agent-driven operations and data stack automation with AI.
Operating Agentic Data Systems
Learn from data platform teams running agentic workloads in production. Real architectures for data quality for AI agents, real trade-offs in AI-powered data transformation, real lessons from operating intelligent data systems.
Vendor-Neutral Architecture Patterns
Building agentic data stack requires repeatable patterns and system-level thinking — not vendor pitches. We focus on architecture blueprints for catalog services for AI agents, agentic ETL pipelines, and data engineering automation.
People don't disappear — their roles evolve
Data Engineers
will focus on architecture, layering, and building better context for agents.
Data Analysts
will define directions, hypotheses, and the right questions, while agents explore and reason autonomously.
How to participate
Join a growing community of data engineers, analysts, and architects shaping the agentic future of data.
Follow
Follow the Agentic Data Stack LinkedIn page and Luma calendar to stay up to date on events, discussions, and new content.
Speak
Submit a talk via our Call for Proposals. Share your experience building, operating, or designing agentic data systems.
Learn
Explore past sessions and shared materials. Learn from real production practices across data platform teams worldwide.
Call for Proposals
10 tracks covering the full spectrum of agentic data systems. Share your experience — submit a talk.
Foundations of Agentic Data Stack
- What makes modern data stack agentic?
- Agentic vs traditional data architectures
- From passive data platforms to active, reasoning systems
- Lessons learned when introducing agents into data stacks
Catalog, Metadata & Context
- Catalog services as agent memory
- Metadata, lineage, and ownership for agent reasoning
- Making data catalogs actionable for agents
- Context modeling for large-scale data systems
Lake Formats, Versioning & Time Travel
- Table formats and branching for agent workflows
- Time travel as checkpoints for agents
- Reproducibility and auditability in agent-driven analytics
- Managing cost and performance with large numbers of versions
Lakehouse/Warehouse Engines & Execution
- Query engines as components for agents
- Plan-aware and cost-aware execution for agent workloads
- Feedback loops between agents and query engines
- Optimizing execution for iterative and exploratory agents
Semantic Layer & Metrics
- Metrics and semantic models as first-class agent knowledge
- Metric reasoning, attribution, and explanation
- Bridging BI semantics and agent planning
- Semantic consistency across agents, dashboards, and pipelines
Data Agents & Agent Frameworks
- Designing data agents over data stacks
- Sub-agents, skills, and task-oriented architectures
- Human-in-the-loop design for data agents
- Evaluating reliability and correctness of data agents
ETL / ELT & Agentic Pipelines
- Pipelines operated or assisted by agents
- Agent-driven data quality checks and recovery
- Backfills, schema evolution, and migration with agents
- From DAGs to adaptive workflows
Agentic Data Stack in Production
- Real-world agentic data stack architectures
- Build vs buy decisions and trade-offs
- Migration stories from traditional data stacks
- What worked — and what didn't — in production
Platform Engineering, DevOps & SRE
- Operating agents and data systems together
- Cost control, isolation, and multi-tenancy
- Observability for agent workflows (logs, traces, feedback)
- Security, permissions, and access control for agents
Governance, Reliability & Evaluation
- Guardrails and approval flows for agent actions
- Governance and compliance in agentic data systems
- Offline and online evaluation of agent workflows
- Continuous improvement with feedback loops