エージェントの夏：Googleの専門家による無料レッスンでエージェントを構築・スケールYour agentic summer: No-cost lessons from Google experts to build and scale agents

Google is offering a set of free, expert-led training resources this summer aimed at helping developers, IT leaders, and other builders move AI agents from concept to production. The initiative, framed by Google as an "agent summer," speaks to one of the most common questions currently circulating in enterprise technology teams: how to actually deploy autonomous agents into live environments rather than keeping them stuck in the prototype stage.

According to the announcement, the program is built around hands-on labs and courses rather than purely theoretical material. The stated goal is to give practitioners a path that takes an AI idea "from a rough sketch to fully autonomous agents running in production." Google says the training draws on the same frameworks and approaches used by its own experts, and that access is provided entirely at no cost during the summer window. That pricing detail is central to the offer, positioning it as a low-barrier entry point for teams evaluating agentic systems.

The courses are powered by Gemini Enterprise Agent Ready, abbreviated as GEAR. This ties the training directly to Google's Gemini family of models, which underpins much of the company's current AI strategy across consumer and enterprise products. The labs reportedly provide blueprints for common but technically demanding scenarios. Two examples highlighted in the source material are designing a system that lets agents interact with external data sources while maintaining strict security guardrails, and creating self-optimizing supply chain workflows. Both cases reflect real-world engineering challenges that go beyond simple chatbot deployments.

The emphasis on security guardrails is notable. Connecting agents to external data and tools is what makes them useful, but it also introduces risk, since an agent that can read databases, call APIs, or trigger actions needs careful controls to avoid data leakage or unintended operations. Training that pairs agent capability with guardrail design appears intended to address a frequent concern among IT leaders who are cautious about granting autonomous software broad access to corporate systems.

For readers less familiar with the space, agentic AI refers to systems that can plan, make decisions, and take multi-step actions toward a goal, often calling external tools or other agents along the way, rather than simply generating a single response. This is a distinct step beyond the question-and-answer pattern of earlier generative AI deployments. Building such systems typically involves orchestration logic, memory or state handling, tool integration, and monitoring, all of which are areas where structured guidance can shorten the learning curve.

The launch fits into a broader industry pattern. Google has been expanding its agent-related tooling through Google Cloud and Vertex AI, and has promoted developer resources such as its Agent Development Kit and support for interoperability standards designed to let agents work with a range of tools and data sources. Competing platforms are pushing in a similar direction: Microsoft has built agent capabilities into Copilot and Azure, Amazon has invested in agent features on AWS, and companies including OpenAI and Anthropic have released their own frameworks and protocols for tool use and agent coordination. Free or subsidized training is a common tactic in this competitive environment, since it helps vendors build familiarity with their platforms and encourages adoption among the developers who influence enterprise purchasing decisions.

The practical value of the program will likely depend on how transferable the skills prove to be. Because the labs are tied to GEAR and the Gemini ecosystem, much of the content appears oriented toward Google's own stack, though the underlying concepts of orchestration, secure tool access, and workflow optimization are broadly applicable across platforms. Organizations weighing the offer may want to consider how closely the training aligns with their existing infrastructure.

For teams that have experimented with agents but struggled to reach production, the combination of hands-on labs, published blueprints, and no upfront cost could serve as a useful on-ramp. As with any vendor-led education, the material reflects a particular product perspective, so participants should treat it as one input among several when designing systems that must meet their own security, compliance, and reliability requirements. Still, the initiative underscores how quickly agentic AI has moved from an experimental idea to a capability that major cloud providers now expect enterprises to operationalize.