Google’s introduction of the Interactions API marks a significant shift in how developers build AI-powered applications, moving away from the traditional “everything prompt” approach toward a more structured, stateful, and scalable architecture. This evolution reflects a growing recognition that complex AI systems require more than just conversational interfaces—they need persistent context, orchestrated workflows, and support for long-running, high-latency tasks. The Interactions API serves as a unified interface for interacting with Gemini models and agents, offering capabilities beyond what the older generateContent API provided. It simplifies state management, tool orchestration, and the handling of asynchronous processes like deep research. Unlike standard chat loops where context is implicitly maintained through a sliding window of tokens, the Interactions API uses a dedicated session resource that stores the full history of a task. This allows developers to reference past interactions via a unique ID, ensuring the model retains accurate context without requiring the entire conversation history to be resent—reducing token usage, improving performance, and lowering costs. One of the most powerful features of the API is its ability to manage stateful, multi-step workflows. For example, a user can start a task, leave, and return later to continue the conversation, with the system automatically recalling prior context. This is achieved by passing the interaction ID into subsequent requests via the previous_interaction_id parameter. The server retrieves the full session history, enabling seamless continuity—critical for applications like onboarding wizards, customer support bots, or research assistants where context drift or hallucination could break the user experience. Perhaps the most groundbreaking aspect of the Interactions API is its support for asynchronous, agentic workflows. Google’s Deep Research agent, for instance, performs complex, multi-stage tasks such as web searching, document analysis, and synthesis—processes that can take several minutes and are impractical to run synchronously. The Interactions API allows developers to initiate such tasks in the background, then poll for status updates at regular intervals. This means users aren’t blocked waiting for results; instead, they can continue working while the system processes the request and notifies them when complete. This capability is demonstrated in a competitive intelligence engine example, where a user inputs a company name and triggers a deep research agent to analyze financial reports, news, and market trends. The agent runs asynchronously, and the developer can monitor its progress without blocking the main thread. Once finished, the system delivers a comprehensive SWOT analysis, complete with structured insights and citations. Beyond research, the API supports multi-modal outputs, such as generating images using models like Gemini 3 Pro Image Preview, and integrates with tools like function calling, structured output, and streaming. It also enables developers to mix different models and agents within a single interaction—using a powerful Deep Research agent for data gathering, then a lightweight model for summarization. The Interactions API is still in beta, and its advanced features like the Deep Research agent are in preview, so caution is advised when deploying in production. However, its architectural design signals a maturation of the AI development landscape. It moves beyond the limitations of simple prompt-based interactions by decoupling reasoning (handled by the LLM) from system architecture (managed by the developer). This shift allows for more reliable, maintainable, and scalable AI applications—especially for domains requiring deep analysis, long-term context, or complex workflows. In short, the Interactions API isn’t just a new tool—it’s a new paradigm. It represents the death of the monolithic “everything prompt” and the birth of a more structured, intelligent, and practical approach to building AI-driven systems.