Summary
In this episode of the AI Engineering Podcast Mark Brooker, VP and Distinguished Engineer at AWS, talks about how agentic workflows are transforming database usage and infrastructure design. He discusses the evolving role of data in AI systems, from traditional models to more modern approaches like vectors, RAG, and relational databases. Mark explains why agents require serverless, elastic, and operationally simple databases, and how AWS solutions like Aurora and DSQL address these needs with features such as rapid provisioning, automated patching, geodistribution, and spiky usage. The conversation covers topics including tool calling, improved model capabilities, state in agents versus stateless LLM calls, and the role of Lambda and AgentCore for long-running, session-isolated agents. Mark also touches on the shift from local MCP tools to secure, remote endpoints, the rise of object storage as a durable backplane, and the need for better identity and authorization models. The episode highlights real-world patterns like agent-driven SQL fuzzing and plan analysis, while identifying gaps in simplifying data access, hardening ops for autonomous systems, and evolving serverless database ergonomics to keep pace with agentic development.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Marc Brooker about the impact of agentic workflows on database usage patterns and how they change the architectural requirements for databases
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what the role of the database is in agentic workflows?
- There are numerous types of databases, with relational being the most prevalent. How does the type and purpose of an agent inform the type of database that should be used?
- Anecdotally I have heard about how agentic workloads have become the predominant "customers" of services like Neon and Fly.io. How would you characterize the different patterns of scale for agentic AI applications? (e.g. proliferation of agents, monolithic agents, multi-agent, etc.)
- What are some of the most significant impacts on workload and access patterns for data storage and retrieval that agents introduce?
- What are the categorical differences in that behavior as compared to programmatic/automated systems?
- You have spent a substantial amount of time on Lambda at AWS. Given that LLMs are effectively stateless, how does the added ephemerality of serverless functions impact design and performance considerations around having to "re-hydrate" context when interacting with agents?
- What are the most interesting, innovative, or unexpected ways that you have seen serverless and database systems used for agentic workloads?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on technologies that are supporting agentic applications?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links
- AWS Aurora DSQL
- AWS Lambda
- Three Tier Architecture
- Vector Database
- Graph Database
- Relational Database
- Vector Embedding
- RAG == Retrieval Augmented Generation
- AI Engineering Podcast Episode
- GraphRAG
- AI Engineering Podcast Episode
- LLM Tool Calling
- MCP == Model Context Protocol
- A2A == Agent 2 Agent Protocol
- AWS Bedrock AgentCore
- Strands
- LangChain
- Kiro
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
