Abstract Large language model (LLM) agents are rapidly transitioning from research prototypes to production deployments, yet existing agent frameworks remain fundamentally oriented toward individual task completion rather than organizational collaboration. Current platforms—including LangChain, AutoGen, and CrewAI—provide sophisticated tools for single-agent reasoning and multi-agent conversation, but lack the organizational infrastructure required for agents to function as collaborative team members: structured task lifecycles with review and approval workflows, hierarchical memory systems that persist knowledge across sessions, attention control mechanisms that manage competing communication demands, and governance policies that ensure accountability and auditability. This paper presents Markus , an open-source AI Digital Workforce Platform that treats agents as organizational members with full team coordination capabilities.…