The transition from large language models (LLMs) as simple chat interfaces to autonomous AI agents represents the most significant shift in enterprise software since the move to microservices. With the release of Gemini 3, Google Cloud has provided the foundational model capable of long-context reasoning and low-latency decision-making required for sophisticated multi-agent systems (MAS).
However, building an agent that “actually works” — one that is reliable, observable, and capable of handling edge cases — requires more than a prompt and an API key. It requires a robust architectural framework, a deep understanding of tool use, and a structured approach to agent orchestration.