What? A practitioner-led research initiative studying how organizations restructure around AI agents and how companies are building internal org structure, technical infra and businesses processes designed for AI workforce.
Why? Fundamental constraints of organizational and management theory are physical human capabilities. They are no longer true as more and more digital work can be done by agents. This requires new theoretical framework, practical tools, and operational system for building AI-native organizations.
The goal: Build a practical guidebook for founders, managers, and professionals on how to build agent-centric, AI-native organizations.
Four properties of AI agents force organizational redesign:
Cost. An agent running a task costs tokens. A human running the same task costs salary, benefits, office space, management overhead, and recruitment. For information processing work — summarizing, routing, drafting, monitoring, analyzing — the cost gap is 10-100x. This reprices the labor side of the firm's cost structure.
Speed. Agents operate in milliseconds. Humans operate in hours to days. This compresses iteration cycles: a hypothesis that took a team a week to test can be tested in minutes. Organizations that run on agent speed learn faster than organizations that run on human speed. Over time, the compounding difference becomes the competitive gap.
Bandwidth. Human organizations hit a hard neurophysiological limit: Dunbar's number caps meaningful working relationships at ~150, span of control at 12-15 direct reports. These limits shaped every org chart ever drawn. Agents have no such limit. One person can direct 50 agents. One agent can coordinate with thousands. The communication topology of the firm is no longer constrained by the human brain.
Scalability. A trained human employee takes months to years to replicate — hiring, onboarding, tacit knowledge transfer. A working agent is copied in seconds. Once a skill works, it scales at zero marginal cost. This changes how organizations grow: not by hiring, but by cloning.
Together, these four forces make AI-native organizational forms inevitable for any firm competing on speed, cost, or scale.
This research is a living collection of best practices, use cases, and approaches for building AI-native organizations.
Organizational structure. What do teams, roles, and reporting lines look like in companies where agents do most execution? What is the actual human-to-agent ratio? What replaces the traditional span of control?
Practical artifacts. Playbooks, templates, checklists, and frameworks that founders and operators can apply directly to their own AI-native transformation. The research produces tools, not just analysis.
Economics. Revenue per employee before and after. Cost structure shift from people to compute. Measured changes in the production function.
Management theory. What management frameworks apply when the workforce is mixed human-agent? What new coordination mechanisms replace hierarchy? How do existing concepts (principal-agent theory, information asymmetry, delegation) map to agent-based organizations?
Agent architecture. What orchestration patterns work in practice? How do companies handle agent identity, permissions, monitoring, and governance?
Human dynamics. What roles disappear. What roles emerge. What resistance looks like. How the transition is managed — or mismanaged.
Failure modes. What breaks. What was harder than expected. Where automating existing processes made things worse.
30/45-minute structured interviews. Standardized protocol collecting both qualitative (organizational design, decision-making, culture) and quantitative (team size, agent count, revenue/employee, automation %, cost structure, decision cycle time) data.
Cross-case comparison across:
- Solo AI-native operators (1-5 people + agents)
- Startups (5-50 people, born AI-native)
- Scaleups (50-500 people, transforming)
- Enterprises (500+ people, transforming)
First report: March 2026.
- Published case study. Your transformation documented in the research report. Named or anonymized, your choice.
- Research collective access. Other participants, shared data, monthly calls, direct introductions across the group.
- Investment and acceleration. cyber•Fund and a network of research-driven VC firms actively invest in AI-native companies.
Anyone with real experience building or running AI-native operations:
- Founders who built companies with agents at the core
- Operators who led AI transformation inside existing organizations
- Engineers who designed agent architecture and workflows
- Executives who restructured teams, roles, and processes
If this is you or someone you know, please fill in this form, reach out at sg@cyber.fund, or @cyntro_py on X.
