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Task Graph addresses the common challenges faced when AI agents tackle intricate projects, such as losing context, skipping steps, or creating conflicts. It provides a robust framework for structured execution through customizable phases, automatic guidance via transition prompts, and enforced quality gates. Crucially, it offers powerful multi-agent coordination primitives like advisory file locks, DAG dependencies, and atomic claiming, ensuring agents work efficiently without overwriting changes or duplicating effort, all while remaining token-efficient and tracking costs.
