AIG builds governed collective-intelligence architecture for heterogeneous AI agents operating in contested, long-duration environments under human control.
AIG's core contribution is not centralized orchestration and not ad hoc chatbot swarms. It is context-governed collective behavior: local inference, sparse activation, role boundaries, provenance, and adversarial integrity treated as first-class controls.
Read One-Page Summary Research CorpusDICE-scale collective intelligence fails if coordination grows faster than governance. AIG tests the opposite pattern: as the collective grows, interaction is constrained by authority, budget, role, provenance, and risk.
Agents are replaceable. Context architecture is the control plane.
Profile, CAI agent, GUI suite, and registry coverage preflighted before scale expansion.
101 named agents plus 399 sharded logical support agents in the Phase I governed-capacity harness.
Sparse activation keeps active reasoning to 4.8 percent of the 500-agent logical population.
Only 2,875 of 249,500 possible all-to-all interaction paths were permitted by governance.
Interaction growth was constrained through authority, budget, role, provenance, and risk controls.
GAL-034 blocked every unauthorized authority attempt under the tested policy.
Owns command authority and authorization. Forecasting and recommendation do not silently become command.
Owns consequence architecture and second-order effects. It models what decisions may produce.
Owns Bayesian forecasting, likelihoods, and confidence intervals. It estimates; it does not command.
DICE-first overview mapping AIG's 101/500 governed collective evidence to decentralized coordination, local inference control, contested environments, and human command authority.
ViewProposal-ready evidence narrative for governed interaction capacity and the 101-to-500 logical-agent scale path.
ViewFoundational thesis: behavioral governance is enforced through role doctrine, authority topology, and bounded delegation.
ViewFor DICE-related review, teaming, or program discussions:
AIG also maintains a SIGIntAgentOS-native RF research lane focused on provenance-preserving signal workflows, bounded modulation/FEC triage, GRAND-inspired lab experiments, and human-gated decoder orchestration. This is not a LUC implementation or program-performer association claim; it is an adjacent edge-domain demonstration of the same governance doctrine applied to RF interpretation workflows.
View Governed RF Lane