コーディングエージェントを使ってエージェント開発ライフサイクルを自動化するAutomate your agent development lifecycle using any coding agent

Google Cloud has published a new installment in its Gemini Enterprise Agent Platform deep-dive series, this time centered on using coding agents to automate the full agent development lifecycle. The subject carries weight because building a production-ready AI agent still involves substantial manual work across planning, coding, testing and deployment, so delegating parts of that pipeline to automated tooling could shorten the path from concept to a shippable system.

Based on the source, the article is framed as a practical, step-by-step walkthrough that starts at the very first step and sets out to teach developers how to build real-world, production-ready agents rather than disposable demos. The written guide is paired with a livestream meant to lead viewers through the entire agentic lifecycle, underscoring the tutorial-driven, hands-on intent of the material.

The core premise is that a coding agent — an AI tool that can read, write and revise code from natural-language prompts — can be applied to the repetitive and structured stages of agent construction. A notable point is that the workflow is described as compatible with any coding agent rather than a single proprietary tool, an approach that appears designed to preserve flexibility and limit lock-in to one assistant.

The agent development lifecycle the guide targets generally spans several phases: defining an agent's goals and behavior, wiring up the underlying model and the tools or APIs it can call, writing and organizing the supporting code, evaluating and testing responses, deploying to a runtime, and monitoring the agent once it is live. Automating these steps with a coding agent is likely aimed at reducing boilerplate, enforcing consistency, and letting engineers concentrate on higher-level design decisions rather than repetitive setup. This is sometimes described as "agents building agents," a pattern that has gained traction as coding assistants have grown more capable.

For context, Gemini Enterprise is Google Cloud's enterprise-focused environment for building and running agents, and it sits alongside a broader set of Google tooling that developers may already recognize. That includes the open-source Agent Development Kit (ADK), which provides a framework for defining agent logic and tool integrations, and Vertex AI, which offers managed infrastructure for deploying and scaling agents. Google has also promoted interoperability standards such as the Agent2Agent (A2A) protocol for communication between agents and the Model Context Protocol (MCP) for connecting agents to external data and tools. A workflow that automates the lifecycle would plausibly touch several of these components, though the exact combination depends on how a team structures its stack.

The move also fits a wider industry trend. Rival platforms have introduced comparable capabilities, including Amazon's Bedr