Sico

Project Sico (Symbiotic Intelligence for Co-evolution) investigates how agentic systems achieve persistent, system-level improvement. The key research theme behind Sico is agentic evolution, the shift from self-improving agents to co-evolving human–AI systems. Its central argument is that reliable improvement emerges not from full autonomy, but from co-evolving human–AI systems, where AI agents and their human operators evolve together through real work.

The project brings together our research, open-source systems, and products around this theme. At its center is an open-source platform for building, managing, and evolving Digital Workers: structured AI labor units that co-evolve with human operators through real production work. The idea emerged from large-scale operational challenges in Microsoft’s internal environments, particularly BPO-style workflows such as black-box testing, where continuous, stable execution at scale is required and traditional script-based automation proves brittle.

In Sico, a digital worker is more than a model with tools. It is a structured, executable capability unit that combines a Cortex for reasoning and planning, an Action layer of domain skills and sandboxed tools, and Memory & Sense for accumulated knowledge and execution experience. Human operators supervise execution quality, intervene when necessary, and guide capability improvement.

This design makes production work itself the engine of improvement. As digital workers take on execution, human roles shift from performing tasks to guiding evolution, and each completed task produces signals that make the workers more reliable over time. Validated through real production workloads, Sico shows that reliability emerges not from static automation, but from continuous co-evolution between human operators and digital workers. Concretely, Sico organizes this co-evolution into three interconnected loops: an Execution Loop that turns operator goals into fully traced agent runs inside observable, replayable sandboxes; an Evolution Loop that distills those traces into reusable, per-project capability; and an Evaluation Loop that attributes the root causes of failures back into learning.

Learn more