🚀 launchReal Shift
Sunday, June 21, 2026
ACCESS GEMINI 3.5 FOR AGENTIC CAPABILITIES IN THE NEW ERA.
Google pushes agent-first Gemini. New action capabilities unlock complex workflows.
4/5
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agent devs, startups, product managers
What Happened
Google just dropped Gemini 3.5, and the headline isn't just "more powerful," it's "more *agentic*." They're explicitly emphasizing new "action" capabilities, signaling a strategic push towards an "agentic Gemini era." This means the model isn't just generating text; it's designed to understand and execute complex, multi-step tasks by interacting with tools and external systems.
Why It Matters
This is a foundational shift for how you'll build with LLMs. Instead of endlessly wrangling prompt chains or hacky parsing, Gemini 3.5 offers more robust, native support for agents. It dramatically lowers the barrier to building complex, autonomous workflows. You can move beyond simple chat interfaces to creating sophisticated AI teammates that can fetch data, send emails, update databases, and orchestrate other services. This accelerates the trend from conversational UIs to powerful, proactive agents that operate across your digital environment.
What To Build
Start designing multi-agent systems that leverage Gemini's action capabilities to automate complex processes. Think research agents that can browse the web, summarize findings, and then draft an executive summary, or customer service agents that can query CRM, look up product info, and then process a refund. Build domain-specific agents for niche industries (e.g., legal review, financial analysis) that integrate with specialized tools. Develop agent orchestration layers that can manage and coordinate multiple Gemini 3.5 agents working together on a larger goal.
Watch For
Pay attention to the robustness and reliability of these "action" capabilities in real-world scenarios – do they hallucinate tool usage? Look for the emergence of an ecosystem of pre-built "actions" or integrations. How does this compare to function calling in other models? Also, keep an eye on how pricing structures adapt for more complex, long-running agentic tasks, which could differ significantly from simple token-based models.
📎 Sources