Fan-out
Distributing a single task or data point to multiple parallel processing units or agents in a graph architecture.
Imagine you have one big job to do, like sorting a huge pile of mail. Instead of one person doing it all, you give the same job to many people at once. Each person sorts a portion of the mail, and then you combine their results. This is a fan-out pattern.
In AI, this means sending the same instruction or question to several different AI agents or processing units simultaneously. They all work on it at the same time, and then their answers are brought together. This can make complex tasks finish faster or allow for more thorough analysis by getting multiple perspectives.
In graph architectures, fan-out refers to a pattern where a single node or task is connected to and sends its output or instruction to multiple downstream nodes or agents. This enables parallel processing, allowing multiple agents to work on the same subtask concurrently or to receive the same input for diverse processing. It is a fundamental technique for building multi-agent systems and distributing workloads efficiently.
The concept of fan-out is relevant as AI labs explore multi-agent systems. Distributing tasks to multiple AI agents allows for parallel processing and potentially faster or more robust results. This is a key pattern for building complex AI workflows.
- Graph Engineering with ADKGoogle · Google Cloud Tech on YouTube ·
- Google Gemini
- Google Gemini's ADK uses a fan-out pattern to distribute tasks across multiple agents in graph architecture, enabling parallel processing for complex AI systems.
- Fan-in
- Fan-in is the opposite pattern where multiple inputs or results are comb
- Broadcasting
- Broadcasting sends the same data to all nodes, but fan-out specifi
- Pipeline
- A pipeline processes tasks sequentially, with the output of one stage
The fan-out pattern originates from general computer science and distributed systems, describing how a single process can initiate multiple parallel processes or send data to multiple destinations.
Ask a chatbot like ChatGPT or Google Gemini to write a short story about a cat who can fly. Then, ask it to also generate three different endings for that story. This shows a simple form of fan-out where one request leads to multiple distinct outputs.
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Questions people ask
- What is the main benefit of fan-out?
- The main benefit is enabling parallel processing, which can significantly speed up complex tasks and allow for more thorough analysis by engaging multiple agents simultaneously.
- How does fan-out differ from sending tasks one by one?
- Fan-out sends one task to many workers at the same time, whereas sending tasks one by one is sequential. This parallel approach is much faster for large workloads.
First seen on Subvolts Sep 10, 2026. Updated Sep 17, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.