Stateless
A stateless system does not store past interaction data, treating every request as independent.
Imagine a vending machine that only accepts exact change for each purchase. It doesn't remember if you bought a soda five minutes ago or if you're a regular customer. Each transaction is completely separate.
In AI, a stateless system works similarly. When you send a request, it processes it without looking at any previous requests you might have made. This makes systems easier to scale because any server can handle any request without needing special information from past interactions.
Statelessness in computing means a server or service does not store client session state between requests. Each request from a client must contain all the information necessary to understand and process the request. This contrasts with stateful systems, which maintain context from previous interactions.
For AI systems, statelessness simplifies load balancing and horizontal scaling. If a server fails, another can immediately take over without losing critical session data. The tradeoff is that all necessary context, such as conversation history or user preferences, must be sent with each request, potentially increasing overhead.
Google Gemini's update highlights the importance of statelessness for scaling AI agent infrastructure. As AI agents become more complex and handle more interactions, stateless designs are crucial for efficient resource management and reliable performance.
- Scaling AI Agent Infrastructure with the MCP Stateless updatesGoogle · Google Developers Blog ·
- Google Gemini
- Google Gemini is updating its AI agent infrastructure with a stateless Model Context Protocol to improve scaling.
The concept of statelessness originates from early network protocols like HTTP, which were designed to be stateless for simplicity and scalability. Source ↗
Open a new chat window in a chatbot like ChatGPT or Google Gemini. Ask it a question. Then, open a second, completely separate chat window and ask the same question. Notice how the chatbot treats both as entirely new conversations, not remembering the first one.
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Questions people ask
- Why is statelessness good for AI?
- Statelessness makes AI systems easier to scale and more resilient. If one part of the system fails, another can take over without losing information about past requests.
- Does stateless mean the AI forgets everything?
- Not necessarily. The core system processing requests is stateless, but it can still access external memory or databases to retrieve past information when needed for a specific request.
First seen on Subvolts Sep 3, 2026. Updated Sep 15, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.