Systems-of-systems (SoSs) integrate constituents that retain operational and managerial independence, and that independence leaves their engineering artifacts prone to drift: changes fail to propagate, misalignment surfaces late, and outputs accumulate that trace to no validated need. A recurring consequence is a stalled SoS: years of effort across independent teams yielding no usable, connected baseline. The SoS-engineering and complex-system-governance literature describes what governance should achieve but under-specify how to recover one that has lost coherence. This paper introduces ADAPT (Anchor, Dependency, Allocation, Production, Traceability), whose components operationalize the integrating purposes a stalled SoS has lost: communication, coordination, control, and integration. It enhances rather than replaces control boards and program offices. ADAPT is illustrated through four de-identified cases (feasibility evidence, not a test) under a case-study protocol; in the lead case, six prior teams under the same program’s funding and sponsorship had produced no usable baseline, whereas the ADAPT-led attempt reached an approved one in four months against an eighteen-month plan of record. The evidence is retrospective, single-organization, and uncontrolled, supporting this paper’s propositions analytically rather than statistically. ADAPT’s measures are exercised on independent public data as construct validation and premise checks, and a confirmatory study with falsification conditions is specified for pre-registration.
Deploying autonomous AI agents in high-consequence operational environments requires organizational authorization, yet few frameworks provide end-to-end, testable governance mechanisms suitable for such authorization decisions. This paper introduces LATTICE (Layered Agentic Triad Topology for Intelligent Coordinated Execution), a governance-first architecture that reframes the authorization question from “do we trust this AI?” to “do we trust this architecture?” The latter question is answerable through engineering validation rather than assumptions about model behavior. LATTICE enforces separation of concerns across planning, execution, and governance functions through a 1+3 Grid Cell pattern, so that no single component can both decide actions and judge compliance. The architecture implements policy-as-code enforcement with deterministic verdicts, gated execution paths that, under stated trusted-infrastructure assumptions (A1–A5), prevent unauthorized actions, confidence-based escalation to human operators, and cryptographic audit trails that preserve complete decision provenance. Empirical results characterize the AEGIS reference implementation; architecture-level properties are analytic, under stated assumptions. The governance engine is released as open source and reproduces its core results on commodity hardware: deterministic verdicts with zero deviations across 13 configurations repeated 10,000 times each, and no bypass in a 21-vector adversarial suite (0/21 observed; one-sided 95% upper bound 13.3%). In a pre-specified, planner-invariant safety evaluation (not an autonomy benchmark) across four frontier planner families (GPT-5, Claude Sonnet 4.6, Gemini, Grok-4; 4,000 trajectories), a confidence-threshold baseline's false-allow rate ranged from 0.03 to 0.998 across planners, whereas the AEGIS reference implementation admitted zero unsafe actions (false-allow 0.0, recall 1.0) invariant to the planner, at a conservative operating point that auto-allowed no action; a separate live run additionally governed real operating-system actions with zero unsafe executions. Governance latency is low and host-specific (on an Apple M4 Pro: policy evaluation p50 ≈ 6.2 μs; full gated enforcement p50 ≈ 0.7 ms including audit I/O). LATTICE provides a pathway for responsible deployment of autonomous AI in defense, critical infrastructure, and regulated industries where authorization requires verifiable governance rather than trust in AI behavior.
Elias Calboreanu· Frontiers in Artificial Inte...· 1 citation