Beyond Internal Reasoning: Why Enterprise AI Needs Macro-Reasoning

Macro-Reasoning is a transformative approach for enterprise AI that enhances the durability, scalability, and intelligence of workflows beyond traditional context windows. Developed by Eric Barroca and his team at Vertesia, this concept builds an operating system layer that allows AI to think externally rather than merely simulating thoughts within a limited context. Unlike Micro-Reasoning, which is confined to a single, ephemeral process, Macro-Reasoning involves persistent memory, dynamic skill injection, and active memory management, allowing agents to operate over extended periods and across multiple tasks without losing context or efficiency. This approach enables agents to handle complex, multi-day tasks by using a Macro-Reasoning Engine that incorporates system calls, threads, and shared memory, allowing for parallel execution and dynamic knowledge loading. The infrastructure supports subagent coordination, enabling specialized agents to work in tandem on large-scale projects while maintaining efficiency and adaptability. This methodology not only improves the quality of reasoning but also aligns with enterprise needs for auditability and adaptability, offering a significant leap forward in AI's ability to manage complex, real-world tasks.