TAG ARCHIVE
team-design
7 MARIA OS blog articles tagged team-design, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
AI Office Operating Model: Design Principles for a Virtual Office Where 10 Teams Work as a Unified Organizational OS
Formalizing the virtual office as a graph-theoretic operating system with inter-team protocols, shared resource management, and graduated autonomy boundaries
This paper presents a comprehensive architecture for a virtual AI office where 10 specialized teams — Sales, Audit, Dev, HR, Legal, Finance, Strategy, Support, QA, and R&D — operate as a unified organizational OS. We formalize inter-team communication protocols as message-passing on a directed graph, define shared resource management through capacity allocation tensors, establish team autonomy boundaries via responsibility cones, and map the entire office to the MARIA coordinate system. The model introduces meeting scheduling agents, knowledge sharing infrastructure, team performance metrics, and conflict resolution mechanisms grounded in organizational graph theory. We prove that office-level governance and team-level autonomy can coexist under a hierarchical gate structure, achieving 89% autonomous operation while preserving 100% accountability traceability.
Team Design Topology: Practical Team Shapes for Throughput, Traceability, and Escalation Control
A design-oriented model for choosing between flat pools, meshes, and review cells
Enterprise agent teams should not be organized by analogy to human org charts alone. This article treats team shape as a controllable systems variable and compares flat pools, dense meshes, and hierarchical review cells using a stylized throughput model. The goal is not to derive a universal theorem, but to give operators a practical way to trade off speed, reviewer load, and responsibility traceability.
Responsibility Distribution in Multi-Agent Teams: Operational Allocation Without Accountability Blind Spots
Treat responsibility as a routing budget for execution, review, and exception handling
When several agents touch one decision, responsibility should be allocated explicitly rather than left implicit in logs or job titles. This article defines a practical responsibility vector for execution, review, approval, and human override. The goal is not to encode legal liability into a formula, but to prevent operational gaps where nobody owns the next action, the next check, or the next escalation.
Conflict Resolution in Hierarchical Agent Teams: Practical Protocols Instead of Overstated Mechanism Proofs
Use structured scoring, bounded escalation, and explicit tie-breaks when agents disagree
Inter-agent conflict is normal in multi-agent teams. The operational challenge is not to eliminate disagreement but to resolve it with bounded delay and acceptable fairness. This article reframes conflict resolution as a protocol design problem: classify the conflict, compare admissible options under a shared scorecard, and escalate only when the local team cannot safely decide.
Cognitive Load Balancing in Human-Agent Hybrid Teams: Scheduling Human Attention as a Limited Resource
A practical workload model for routing review to people who still have real attention left
Human oversight fails when review demand is treated as infinite capacity. This article presents a practical control model for supervisor load, priority routing, and rest-aware scheduling. The emphasis is operational: estimate available attention, protect high-priority reviews, and avoid the common failure mode where humans are technically in the loop but cognitively saturated.
Skill Complementarity in Agent Ensembles: A Stable Coverage Metric for Team Composition
Replace brittle convex-hull claims with coverage, dispersion, and backup depth
Selecting the highest-scoring individual agents often yields homogeneous teams that leave important parts of the problem space uncovered. This article replaces an overly brittle convex-hull formulation with a more stable Skill Complementarity Index based on skill coverage, pairwise dispersion, and backup depth. The result is easier to compute, easier to interpret, and better aligned with real team-design decisions.
Fault-Tolerant Team Architectures: Reliability Patterns for Multi-Agent Systems Without Mathematical Overclaim
Use redundant role coverage, graceful degradation, and recovery drills instead of fragile point estimates
Multi-agent teams fail when a required role disappears and nobody can safely take over. This article reframes fault tolerance around role coverage, standby design, and recovery speed. Rather than overpromising precise MTTF values, it focuses on the operational question that matters: how many failures can the team absorb before a critical function becomes unstaffed?