Google Cloud’s Post

Agentic patterns are getting clearer every week. What stands out here is the shift from single-agent demos to real multi-agent orchestration that enterprises can actually deploy. Excited to see Google Cloud pushing architecture-level guidance instead of just tool releases, that’s what accelerates adoption.

These architecture patterns are exactly what enterprises need as they move from single-use copilots to full multi-agent ecosystems. The focus on domain-specific agents, orchestration, and governance aligns perfectly with how real adoption is happening across retail, manufacturing, healthcare, and finance. Agent Engine is quickly becoming a powerful foundation for enterprise-grade agentic AI.

Great work! Collaborating with SI partners to define agentic architecture patterns is a powerful step toward helping enterprises adopt scalable, production-ready AI agents across industries.

Love seeing hands-on collaboration with SI partners—building an ecosystem of agents across industries sounds way more exciting than just spinning up yet another proof of concept! Enterprise AI agent design isn’t just about the tech; it’s about ensuring every use case, from retail to healthcare, gets reliable and scalable intelligence. For teams seeking a head start, platforms like https://www.chat-data.com/ offer ready-made solutions for multi-agent workflows, cross-domain integration, and compliance. Whether you're handling sensitive medical data or streamlining manufacturing, Chat Data brings serious horsepower to agentic architecture—no custom Kubernetes headaches required.

Thanks to Google Cloud team for sharing this insightful post I wrote a summary post about it here https://www.linkedin.com/posts/mahmoudrabie2004_forabraiabrarchitects-forabrcloudabrarchitects-activity-7386137864092237824-exvq?utm_source=share&utm_medium=member_android&rcm=ACoAAANl-ukBNmz5qhlJOrQNtSt-ajHYfLd2Bvc

The Memory Bank component in this architecture caught my eye. We've been wrestling with session state across our multi-location deployments, and having that as a dedicated service rather than bolting it onto the agent runtime would've saved us weeks of debugging. Curious if the SI partners found specific patterns for handling memory persistence when agents need to pick up context across different user sessions?

Multi-agent architectures are becoming essential for complex enterprise workflows. The key challenge is orchestrating these agents effectively while maintaining clear accountability and observability across the system.

Schneider Larbi & David Peterside

For those already using Cloud Run or GKE, what’s been your experience managing agent memory yourself versus using Agent Engine’s built-in memory? Worth the migration?