Build an AI Research Agent with Google Interactions API & Gemini 3

2024-09-27 · Source: unwind ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

Google has introduced the Interactions API and Gemini Deep Research, enabling the creation of autonomous, multi-phase AI research agents. This new API paradigm shifts from stateless request-response cycles to server-side state management, background execution for long-running tasks (2-5 minutes), and seamless context handoffs between different models. A tutorial demonstrates building an AI Research Planner & Executor Agent using a three-phase workflow: Gemini 3 Flash generates research plans, the Deep Research Agent conducts web investigations, and Gemini 3 Pro synthesizes findings into executive reports with auto-generated infographics. The system uses `previous_interaction_id` to maintain context across phases, allowing for complex, stateful workflows in production-ready agentic applications. Prerequisites include Python 3.12 and a Gemini API key.

Key takeaway

For AI Engineers building sophisticated agentic applications, the Google Interactions API fundamentally changes how you manage state and orchestrate multi-model workflows. You should explore using `previous_interaction_id` to chain interactions and `background=True` for long-running tasks, significantly simplifying context management and enabling more robust, production-ready AI systems. This approach allows for more complex, multi-step processes without manual context passing.

Key insights

Google's Interactions API enables stateful, multi-phase AI agents by managing server-side context and background execution.

Principles

• Server-side state management simplifies complex agentic workflows.
• Asynchronous execution supports long-running, multi-step tasks.
• Model chaining with context preserves continuity across phases.

Method

The proposed method involves a three-phase workflow: planning with Gemini 3 Flash, deep research with Deep Research Agent, and synthesis with Gemini 3 Pro, all orchestrated via the Interactions API's `previous_interaction_id` for context.

In practice

• Use `previous_interaction_id` for stateful context in multi-turn agents.
• Employ `background=True` for long-running API calls.
• Integrate different Gemini models for specialized tasks.

Topics

• AI Research Agents
• Google Interactions API
• Gemini Models
• Stateful AI
• Multi-Phase Workflows

Code references

• Shubhamsaboo/awesome-llm-apps
• Shubhamsaboo/awesome-llm-apps

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

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• Gemini Interactions API Quick Start — The Gemini Interactions API unifies agentic application development with server-side state, tool orchestration, and multimodal capabilities.
• The Biggest Announcements from Google I/O 2026 — Google I/O 2026 signals a fundamental shift to AI agents that act on queries, not just answer them, across its ecosystem.
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Editorial summary, takeaway, and curation by AIssential. Original article published by unwind ai.