From Chatbots to Autonomous AI Workers: Why Google’s New Interactions API Is a Game-Changer for Developers
Artificial intelligence just got a major infrastructure upgrade — and developers should take notice.
Google this week launched its new Interactions API, a fresh interface that promises to transform how developers build AI applications. No longer constrained by simple prompt-and-reply models, the Interactions API brings stateful conversations, long-running task execution, and built-in agent logic into a unified system, enabling next-generation AI experiences. (Venturebeat)
Breaking Out of the “Stateless” Trap
For years, most AI APIs — including Google’s own generateContent endpoint — operated in a stateless mode: you send a prompt, get a response, but the model doesn’t remember anything unless you store and resend the entire history yourself. That made it hard to build applications that require memory, multi-step reasoning, or prolonged workflows. (Venturebeat)
The Interactions API changes this by introducing server-side state management. Instead of resending long conversation histories on every call, developers can pass a simple previous_interaction_id, and Google’s infrastructure retains context, tool outputs, and internal reasoning on its side. This shift moves developers closer to treating AI not just as a text generator, but as a stateful system capable of thoughtful, adaptive behavior. (Venturebeat)
Stateful Agents: AI That Can Actually Do Things
Beyond better memory, the Interactions API supports long-running tasks and background execution. Complex jobs like web research or data synthesis — which might otherwise time out in standard APIs — can now be queued, run asynchronously, and queried for results later. This effectively turns the API into a job manager for AI tasks, a critical innovation for building autonomous agents. (Venturebeat)
Out of the box, Google is also rolling out built-in agents, most notably Gemini Deep Research — a sophisticated AI designed to autonomously gather, analyze, and synthesize information across documents and web sources. Developers can plug this directly into apps using the Interactions API, dramatically reducing the engineering effort needed to build research-capable workflows. (blog.google)
A Unified Interface for Models and Agents
One of the most striking aspects of Google’s approach is that it treats core models and agentic systems under the same API endpoint. Whether you’re calling a standard Gemini model or invoking a built-in agent like Deep Research, the same Interactions interface handles it — simplifying development and reducing fragmentation. (Google Developers Blog)
The API also embraces the Model Context Protocol (MCP), an open ecosystem standard that lets AI models call external tools and services directly without custom glue code. This means AI workflows can integrate with databases, web services, or other systems more seamlessly. (MLQ)
Not Just Another API — A New Paradigm
Google’s move mirrors a broader industry shift toward agentic AI — systems that reason, plan, and act over time — but with its own philosophical twist. Unlike some competitors that compress conversation history into opaque tokens, Google’s hosted state model aims for inspectability and composability, giving developers more transparency and control over what’s happening under the hood. (Venturebeat)
As AI moves beyond simple chatbots toward autonomous knowledge workers and embedded research engines, the Interactions API could become a foundational piece of that next wave of applications.
Glossary
Stateful Conversations – A model interaction where past context is retained and reused automatically, rather than requiring clients to resend all history every time.
Agentic AI / Agents – Autonomous AI components capable of multi-step planning, tool use, and long-running tasks, as opposed to simple prompt responses.
Model Context Protocol (MCP) – An open standard that allows AI systems to interact with external tools and services, facilitating richer workflows.
Background Execution – The ability for an AI task to run asynchronously on the server side, letting clients poll for completion later.
