Scaling patterns for self-organizing multi-agent clusters with Kiro

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Most multi-agent systems today follow the same shape: a supervisor agent breaks a task down, hands the pieces to subagents, and stitches the results back together. This is how Kiro CLI delegates to subagents, and what the Strands Agents SDK gives you primitives for with graphs and agents-as-tools. It is a good default. One process holds the plan, so behavior stays predictable and every result passes through a single gate. That one process is also the limit. Every assignment and every result flows through the supervisor, so its context window caps how much work the system can hold at once. If it dies, the run dies with it. And because a single planner fixes the decomposition upfront, you get one take on the problem, multiplied by N workers. Plenty of distributed systems still coordinate centrally, and should. But the alternative has been around for decades: let participants converge through shared state instead. We wanted to know what happens when you apply that move to artificial intelligence (AI) agents, so we built kiro-flock, an open-source reference implementation. It runs clusters of Kiro CLI agents on Amazon Elastic Compute Cloud (Amazon EC2) with nothing between them but an Amazon Simple Storage Service (Amazon S3) bucket. No orchestrator, no message bus. Agents coordinate by reading each other’s append-only logs. This post explains the pattern and gives you enough to deploy the sample and watch a cluster converge yourself. When to use this pattern Architecture has to match the task. In the 2025 study “Towards a Science of Scaling Agent Systems” of 260 …

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