The Death of the “Everything Prompt”: Google’s Move Toward Structured AI

2026-02-09 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

Google has released its new Interactions API, an extension to its existing generateContent API, designed to build more structured, interactive, and stateful AI-driven applications. This API addresses the limitations of standard LLM chat loops, where state is implicit and prone to context loss. The Interactions API simplifies state management, tool orchestration, and long-running tasks by introducing an "Interaction" resource that acts as a persistent session record. It enables developers to manage conversational context explicitly, integrate high-latency agentic capabilities like Google's Deep Research, and handle asynchronous operations. The article demonstrates its use for basic queries, multimodal image generation with Gemini 3 Pro Image Preview, stateful conversations, and orchestrating a competitive intelligence engine using the deep-research-pro-preview-12-2025 agent to perform SWOT analysis.

Key takeaway

For AI Engineers building robust, stateful applications beyond simple chat, the Google Interactions API offers critical architectural scaffolding. You should explore its capabilities for explicit state management and asynchronous agent orchestration, especially when integrating high-latency services like Deep Research. This API allows you to decouple the LLM's reasoning from your application's architecture, leading to more reliable and performant AI products.

Key insights

Google's Interactions API provides structured state management and asynchronous agent orchestration for complex AI applications.

Principles

• Explicit state management improves AI application reliability.
• Decouple LLM reasoning from application architecture.
• Asynchronous agents enable complex, long-running tasks.

Method

The Interactions API uses an "Interaction" resource to store session history. To maintain state, pass a previous_interaction_id. For asynchronous tasks, set background=True and poll for status.

In practice

• Use previous_interaction_id for stateful conversations.
• Integrate Deep Research agents for complex data synthesis.
• Poll for results of long-running background tasks.

Topics

• Google Interactions API
• AI State Management
• Agentic AI
• Asynchronous AI
• LLM Orchestration

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• Build an AI Research Agent with Google Interactions API & Gemini 3 — Google's Interactions API enables stateful, multi-phase AI agents by managing server-side context and background execution.
• No AI Agent Orchestration Needed? What? — Modern frontier LLMs can self-orchestrate complex procedural tasks more effectively via in-context prompting than with external agent frameworks.
• AsyncTool: Evaluating the Asynchronous Function Calling Capability under Multi-Task Scenarios — AsyncTool evaluates LLM agents' asynchronous tool calling in multi-task scenarios with realistic latency.
• Want to Build AI Agents? Start by Calling an LLM API Yourself — Direct LLM API calls are the fundamental building blocks for all AI agents.
• PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence — PPAI enables personalized LLM agents on edge devices to collaborate via a P2P network, optimizing task delegation and load balancing.
• One on One with Marc Benioff — AI agents and conversational interfaces like Slack are transforming work, making human-agent collaboration central to enterprise operations.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.