TAG ARCHIVE
agent-teams
3 MARIA OS blog articles tagged agent-teams, 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.
Collective Calibration Dynamics: How Agent Teams Achieve Shared Epistemic Accuracy in MARIA OS
A formal analysis of how multi-agent teams calibrate collective confidence through structured interaction, showing why individual calibration is necessary but insufficient for team-level epistemic accuracy and how topology governs convergence
Individual calibration error measures how well one agent's stated confidence matches realized accuracy. In collaborative settings, however, a distinct phenomenon appears: collective calibration, where team-level confidence must track team-level accuracy. This paper defines collective calibration error as a metric that cannot be reduced to aggregated individual calibration, proves that individually well-calibrated agents can still form a poorly calibrated team under certain interaction topologies, and derives sufficient graph conditions for convergence. We validate the framework on MARIA OS deployments with 623 agents across 9 zones, showing a 41.7% reduction in collective calibration error via topology-aware reflection scheduling.
Detecting Groupthink in Agent Teams: Persistent Homology for Blind-Spot Alerts
Topological signals expose hidden coverage gaps and groupthink risk that pairwise diversity metrics can miss
Persistent homology tracks coverage holes across scales to flag latent team blind spots earlier.
Productive Disagreement Protocol for Agent Teams: Structured Dissent for Higher-Quality Decisions
Operationalize evidence-backed dissent, validation diversity, and anti-groupthink interventions
Structured disagreement channels dissent into testable claims, improving decision quality without collapsing throughput.