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The challenges of building self-evolving agentsSelf-evolving agents are crucial because they overcome the limitations of frozen language models. Once a model is deployed, its parameters remain fixed, restricting it to the knowledge encoded during training and whatever fits in its immediate context window.
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This enables deterministic step sequencing through code positioned before a step's initial await. Our @Step() decorator implements this by immediately assigning sequential identifiers from workflow context before any potential await operations. Consequently, step identifiers allocate deterministically according to their submission order to asyncio.gather, ensuring task-processor alignment.
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