ref: https://arxiv.org/pdf/2509.00625

webpage: https://www.agintai.com/

Summary

Agint differs from workflow agents as a compiler agent (like IDE). It translates natural language into an executable, result-oriented DAG, where operating at the graph level enables parallel execution and removes the constraint of linear, chain-based generation. This approach emphasizes result-oriented execution: workflows are not fixed upfront but are dynamically generated, refined, and partially executed at runtime.

Agint feels similar to Windsurf Cascade, where “deep thinking” is shifted from the model into an AI-assisted DAG. If this direction holds, does “divide & conquer” through workflows outperform ever larger, deeper-thinking models?

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Natural Language Input

LLM Generated DAG

Each node (Available for prompt modification)