In this session, gain the skills needed to deploy end-to-end agentic AI applications using your most valuable data. This session focuses on data management using processes like Model Context Protocol (MCP) and Retrieval Augmented Generation (RAG), and provides concepts that apply to other methods of customizing agentic AI applications. Discover best practice architectures using AWS database services like Amazon Aurora and OpenSearch Service, along with analytical, data processing and streaming experiences found in SageMaker Unified Studio. Learn data lake, governance, and data quality concepts and how Amazon Bedrock AgentCore and Bedrock Knowledge Bases, and other features tie solution components together.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Agents need both fresh data (real-time signals) and rich context (historical, semantic). MCP and RAG are the two delivery mechanisms; they coexist rather than compete.
Why it matters
A great agent on stale or wrong data is just confidently wrong, faster. The data stack is the constraint, not the model.
The hard parts
Data quality, lineage, freshness, and access control are not solved by "buy a vector DB." They're solved by data engineering discipline that most orgs lack.
Playbook moves
(1) Inventory data sources by latency tier (sub-second / minutes / hours / batch). Map agents to the tier they need. (2) Audit what data the agent *actually* uses, not what you intended. (3) Apply governance at the source, not at the agent — agents will route around perimeter controls.
The surprise
RAG retrieval quality is dominated by chunking strategy, not embedding model. Boring but true. Spend a week on chunk size, overlap, and semantic boundaries before you spend a dollar on a fancier embedder.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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