AI agents are reaching production faster than the infrastructure beneath them. They make decisions, call tools, and change behavior from one interaction to the next, and they need durable memory to do it. The hard problem has moved from the model to the data layer.
Most teams cover the gap with a stitched stack of transactional, vector, cache, and object stores. The result: inconsistent data, split audit trails, rising costs, and little visibility into what agents actually do.
In this webinar, AWS, Grafana Labs, and TiDB show how to close that gap with full observability from a single agent conversation to an entire fleet, and one governed system for agent memory, operational data, and analytics on AWS.
What you’ll learn:
- Why agent projects stall between proof of concept and production.
- The five questions every team running agents in production must be able to answer.
- How to connect agent behavior, token spend, and quality to the infrastructure underneath.
- What a stitched data stack costs you in consistency, auditability, and margin.
- How governed memory gives Amazon Bedrock AgentCore agents provenance and a full audit trail.
- Lessons from AI-native companies running agents on TiDB, including Manus.
Speakers:
Seenu Brahmarouthu, Principal, AI Category Lead, AWS
He works on how organizations build, deploy, and scale agentic AI on AWS, from Amazon Bedrock AgentCore and Strands Agents to the AI agents and tools available in AWS Marketplace. In this session, he frames the agentic AI opportunity and what it takes to cross from prototype to production.
Shawn Pitts, Staff Technical Marketing Manager, Grafana Labs
He focuses on observability for AI agents in production, helping teams understand what their agents are doing, how they perform, what they cost, and why they fail, from a single conversation to an entire agent fleet.
Christopher Hofmann, Senior Business Development Manager, TiDB
He works with teams scaling multi-tenant SaaS, payments, real-time risk and fraud, and first-generation agent workloads, helping them evaluate the data layer behind production AI and the hidden cost of stitched-together database stacks.
Bernard Kavanagh, Principal Solutions Architect, TiDB
He focuses on observability for AI agents in production, helping teams understand what their agents are doing, how they perform, what they cost, and why they fail, from a single conversation to an entire agent fleet.