Enterprise AI is only as good as the data it reasons over. Getting a fast answer is easy. Getting a correct answer is the hard part.
LangChain and DataHub provide the AI and data infrastructure to power natural language text-to-SQL workflows at enterprise scale. LangChain and LangSmith provide the agent engineering backbone that enables reliable orchestration, step-by-step tracing, and iterative refinement of AI workflows in production. DataHub provides the context layer that makes those workflows trustworthy: with business definitions, data lineage, semantic metadata, and live data quality signals that ensure AI-generated answers are grounded in truth.
In this joint session, LangChain and DataHub show how to build AI agents for data querying.
What you’ll learn:
- How LangChain’s deep agents go beyond simple retrieval to perform multi-step reasoning, tool use, and planning over complex data environments.
- How DataHub ensures agents have the semantic context, governance guardrails, and trust signals they need to return answers people can actually rely on.
- How AWS provides the scalable, secure foundation for deploying agentic AI workloads in the enterprise.
Whether your team is building its first AI agent or scaling LLM-powered applications across a complex data estate, this session delivers a practical architecture for grounding AI in data that is accurate, governed, and ready for enterprise use.
Who should attend: Data platform engineers, analytics engineers, data governance leads, and enterprise architects evaluating AI-native analytics on AWS.