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This is an AI coding-agent update from Kiro that lets a model plan a multi-agent workflow, which the runtime then executes. Each step gets a fresh context window, so a reviewer assesses the work without inheriting the coder's reasoning. The claims are unbenchmarked so far, so readers should watch for independent evidence that the approach yields better results than supervised sessions.
Source coveringKiro

Introducing Kiro workflows

Kiro8 min read

Today, we’re introducing Kiro workflows, allowing you to carry out complex tasks from start to finish with multiple agents and less supervision. We’ve been building Kiro itself with workflows, including the new cloud configuration, cloud sessions, and most of the workflow experience.

Today, we’re introducing Kiro workflows, allowing you to carry out complex tasks from start to finish with multiple agents and less supervision. We’ve been building Kiro itself with workflows, including the new cloud configuration, cloud sessions, and most of the workflow experience. Before workflows, we’d ask an agent to implement a change, then come back with “now do a code review of these changes” and later “address the code review findings.” We spend much of the session supervising, prompting again when it skips a step and reminding the agent of earlier decisions. Within a session, the model controls the sequencing, but a model’s attention is limited. Everything we agreed on sits in the same context window that fills with tool output and file contents, so we also step in when it forgets what still needs to be done. We work around this by splitting the job across separate sessions and keeping the plan in a file that persists across those sessions,…

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