AI agents are only as effective as the data they can access. Yet most enterprise architectures still depend on batch pipelines, fragmented systems, and tightly controlled operational environments that limit real-time context.
In this technical deep dive, Striim and Google Cloud outline a practical, production-ready architecture for streaming real-time operational data into Google Cloud to power AI at scale.
We’ll examine how low-latency Change Data Capture (CDC) enables continuous movement of trusted enterprise data from on-prem, hybrid, and multi-cloud environments into BigQuery and Cloud Storage—creating a dynamic foundation for analytics, model training, evaluation, and serving on Vertex AI.
The session goes beyond reference diagrams. We’ll explore how continuously refreshed data supports goal-directed reasoning in AI agents, improves memory retention through up-to-date context, and enables dynamic planning in real-world operational workflows. Just as importantly, we’ll address governance, observability, and architectural flexibility—so teams can modernize without compromising compliance or performance.
If you're building AI systems that need fresh enterprise context to act safely and effectively, this session will give you a blueprint grounded in real-world constraints.
What You'll Learn:
1️⃣ Real-Time Data as Agent Context: How low-latency CDC pipelines create continuously updated data products that power training, evaluation, and real-time inference on Vertex AI.
2️⃣ Architecture for Hybrid and Multi-Cloud Enterprises: Patterns for securely streaming operational data from on-prem and distributed systems into BigQuery and Cloud Storage without disrupting core systems.
3️⃣ From Data Movement to AI Activation: How fresh, governed data enables agent memory, dynamic decision-making, and modular AI workflows at scale.
4️⃣ Governance, Observability, and Control: How to maintain compliance, enforce access controls, and monitor data pipelines while supporting production-grade AI systems.
5️⃣Future-Proof Platform Design: How to architect for flexibility—so your AI stack can evolve as models, agents, and enterprise requirements change.
This is an architecture-led session for data and AI leaders who want to move beyond experimentation and build enterprise AI systems that operate in real time—securely, reliably, and at scale.
Register here
