Google CloudでAIエージェントを構築してみた — 誰も教えてくれないこと

Introduction: At Google Cloud NEXT 26, one of the most interesting shifts wasn’t just about faster models or better APIs — it was about how developers are starting to build agents instead of traditional applications. Instead of writing step-by-step logic, we’re now defining behavior, constraints, and goals. I decided to explore this idea by trying to build a simple AI agent using Google Cloud tools. What I found was both exciting and slightly chaotic. What I Tried I attempted to create a basic AI agent that could: 1.Take a user query 2.Process it using an AI model 3.Return a structured response The idea sounded simple. In reality, the challenge wasn’t building the agent it was controlling it What Worked Well: Google Cloud’s ecosystem makes it relatively easy to get started: Integration with AI models is fast APIs are well-documented Deployment options like serverless reduce setup overhead Within a short time, I had a working prototype that could respond intelligently to inputs. The Real Problem: Lack of Guardrails Here’s where things got interesting. The agent didn’t always behave predictably. Sometimes it: Ignored instructions Drifted away from the intended task Produced inconsistent outputs This made one thing very clear: The hardest part of building AI agents is not intelligence — it’s control. Key Insight: Context > Code One major takeaway is that traditional coding skills are no longer enough. To build reliable agents, you need: Clear scope definitions Strong prompt design Constraints that guide behavior Without these, the agent becomes unpredictable over time. This aligns with a broader idea emerging from Cloud NEXT ’26: Developers are shifting from writing logic to designing behavior. What Could Be Improved While the tools are powerful, there are still gaps: No built-in “safety boundaries” for agents Limited guidance on structuring long-running behavior Debugging agent decisions is still difficult These are critical areas that need improvement for real-world applications. Conclusion Google Cloud’s direction toward AI-driven development is clear, and it’s genuinely exciting. However, building with AI agents requires a different mindset: Less focus on code More focus on control, context, and constraints We’re not just building applications anymore — we’re designing systems that think. And honestly, we’re still figuring out how to do that properly. Introduction: At Google Cloud NEXT 26, one of the most interesting shifts wasn’t just about faster models or better APIs — it was about how developers are starting to build agents instead of traditional applications. Instead of writing step-by-step logic, we’re now defining behavior, constraints, and goals. I decided to explore this idea by trying to build a simple AI agent using Google Cloud tools. What I found was both exciting and slightly chaotic. What I Tried I attempted to create a basic AI agent that could: 1.Take a user query 2.Process it using an AI model 3.Return a structured response The idea sounded simple. In reality, the challenge wasn’t building the agent it was controlling it What Worked Well: Google Cloud’s ecosystem makes it relatively easy to get started: Integration with AI models is fast APIs are well-documented Deployment options like serverless reduce setup overhead Within a short time, I had a working prototype that could respond intelligently to inputs. The Real Problem: Lack of Guardrails Here’s where things got interesting. The agent didn’t always behave predictably. Sometimes it: Ignored instructions Drifted away from the intended task Produced inconsistent outputs This made one thing very clear: The hardest part of building AI agents is not intelligence — it’s control. Key Insight: Context > Code One major takeaway is that traditional coding skills are no longer enough. To build reliable agents, you need: Clear scope definitions Strong prompt design Constraints that guide behavior Without these, the agent becomes unpredictable over time. This aligns with a broader idea emerging from Cloud NEXT ’26: Developers are shifting from writing logic to designing behavior. What Could Be Improved While the tools are powerful, there are still gaps: No built-in “safety boundaries” for agents Limited guidance on structuring long-running behavior Debugging agent decisions is still difficult These are critical areas that need improvement for real-world applications. Conclusion Google Cloud’s direction toward AI-driven development is clear, and it’s genuinely exciting. However, building with AI agents requires a different mindset: Less focus on code More focus on control, context, and constraints We’re not just building applications anymore — we’re designing systems that think. And honestly, we’re still figuring out how to do that properly.