Hack2Skill Gen AI Academy APAC Edition | Hybrid Quarter - 3 | Cohort 3 (Aug '26 - Sep '26)
There is a massive difference between building a single AI chatbot and deploying a multi-agent orchestration system that can run a business at a national scale.
For the past few weeks, as part of Cohort 3 of the Hack2Skill Gen AI Academy, I’ve been bridging that exact gap. Our theme for this Hybrid Quarter has been "Accelerate AI with Cloud Run," and it has been nothing short of transformative. Instead of just writing scripts in a sandbox, we were challenged to take a fictional growing business—a local coffee shop—and scale it using a connected system of AI agents deployed entirely on Google Cloud.
Here is how we took our coffee shop from its first smart customer interaction all the way to a lean, data-driven national franchise.
Lab 1: The First Sip (Personalized Customer Experience)
Every great business starts with the customer. In our first lab, the goal was to master basic AI agent deployment by creating a highly personalized customer experience.
We built our first generative AI agent and containerized it to run on Google Cloud Run. This agent didn't just take orders; it remembered customer preferences, recommended seasonal blends based on past purchases, and engaged in natural, fluid conversation.
The Key Takeaway: Cloud Run’s serverless architecture meant our customer-facing agent could instantly scale from zero to handle a morning rush of concurrent users without us having to provision a single server. We had successfully deployed our first endpoint.
Lab 2: Spilling the Beans on Growth (Data-Driven Location Intelligence)
With our local shop thriving, it was time to expand. Lab 2 introduced us to the complexities of multi-agent orchestration.
Instead of guessing where to open our next locations, we built a system of specialized AI agents working in tandem:
The Demographic Agent: Analyzed public census and foot traffic data.
The Real Estate Agent: Scraped and evaluated commercial property listings.
The Competitor Agent: Mapped out existing coffee shops in the target area to find market gaps.
We orchestrated these agents to communicate with each other, synthesize their findings, and output a highly ranked list of optimal new store locations. Running this complex, long-running workflow on Cloud Run taught us how to manage state, handle timeouts, and string together microservices so that the output of one agent seamlessly fed into the prompt of another.
Lab 3: Running Lean (Automating Daily Operations)
Expanding to a national scale introduces massive operational bloat. In our final lab, we aimed to automate daily operations to run the franchise as leanly as possible.
We deployed an overarching "Manager Agent" that monitored the entire network. This system dynamically adjusted inventory orders based on weather forecasts (e.g., ordering more cold brew beans ahead of a heatwave), scheduled staff based on predicted foot traffic, and automatically drafted marketing copy for regional promotions.
The Key Takeaway: By breaking down massive operational tasks into small, event-driven Cloud Run services triggered by Pub/Sub, our multi-agent system became a resilient, automated brain for the entire national operation.
Final Thoughts from Cohort 3
The journey from a single Python script to a cloud-native, multi-agent orchestration system is steep, but incredibly rewarding. The Hack2Skill Gen AI Academy APAC Edition didn't just teach us how to write prompts; it taught us how to architect enterprise-grade AI solutions.
By building our coffee shop empire on Google Cloud Run, we learned firsthand how to deploy scalable, secure, and highly available AI agents. The future of business isn't just about using AI—it's about orchestrating it.
Have questions about how to deploy your own Gen AI agents on Google Cloud Run? Drop a comment below!
