Expert AgentChain of Command.
Five tiers of increasing abstraction and decreasing autonomy. Six modality-specialized inference agents, each shadowed by an adversarial stress-test team. Every finding cryptographically signed before it reaches command. The human authorizes. The system proposes.
Six design principles.Non-negotiable.
Command controls the loop.
The system proposes; the commander disposes. Never inverted. This is an architectural invariant, not a configuration option.
Edge-first, cloud-optional.
Every safety-critical inference runs locally on the target hardware. The cloud is never on the critical path. DDIL is the assumed baseline.
Consensus before propagation.
No finding from a single agent reaches ELA. Every finding is stress-tested by independent evidence paths before escalation.
Deterministic on the safety path.
Temperature zero on classification. Outputs are auditable, reproducible, and traceable to sensor evidence. A classification is reproducible or it does not ship.
DDIL as default.
The comms pipe is assumed to fail. Architecture is designed for that case, not the other. Store-and-forward ensures no data is lost on restoration.
Policy as primitive.
Rules of engagement and doctrine are not guardrails — they are first-class constraints on every agent's decision surface.
Five tiers.One chain.
Lower tiers observe raw sensor data and produce local hypotheses. Higher tiers aggregate, verify, stage logistics, and request authorization. Autonomy decreases as abstraction rises.
Multi-modal raw sensor acquisition. Each modality normalized locally before ingest — SAR speckle reduction, thermal background subtraction, gravity baseline correction.
Six modality-specialized inference models. Each owns one sensor stream. Each produces a structured hypothesis: class, bounding, confidence, evidence weights. No agent acts beyond its specialization.
Each Expert Agent is shadowed by a pairwise stress-test team of three adversarial models: a counter-hypothesis model, a calibration model, and an evidence integrity model. A consensus signature is emitted to ELA only when all three checks clear and cross-modality agreement is established.
- Expert Agent confidence above per-modality threshold
- Stress-test team raises no objection
- At least one additional agent corroborates cross-modality
- Evidence integrity model finds no compromised input
ELA aggregates Tier 03 consensus signatures into unified incident objects. It computes the optimal engaging unit (positional advantage, readiness, munition availability), stages secondary and tertiary units, determines the notification web, and prepares the comms pattern. ELA does not authorize. ELA prepares the path — so that when command authorizes, execution is already staged.
The human commander receives the ELA-prepared package: verified consensus, optimal pairing, staged response — all as read-only context. The commander's explicit authorization is the only write to the kill chain. Base · Forward · Secondary · Tertiary nodes support delegation in sequence if the primary node is unreachable.
End-to-end budgetat P99.
Every modality.One Expert.
Each Expert Agent is a narrow, modality-specialized model trained to interpret a single sensor stream at high fidelity. No Expert Agent acts unilaterally. No Expert Agent acts beyond its specialization.
Analyzes synthetic-aperture radar returns for hydrodynamic wakes, surface roughness anomalies, and subsurface displacement signatures. First-line maritime and terrestrial discrimination. Operates continuously against baseline wave-state model from ESEA.
Detects and classifies thermal signatures — reactor plumes, engine bloom, propellant ignition, warm-water reactor outflow. Separates intent signals from background. Critical for pre-boost launch detection and submarine reactor identification.
Passive gravity anomaly discrimination. Computes mass-based inference of submarine hull class, approximate location, and depth regime from displacement alone. Undetectable by active countermeasures — the sensor cannot be jammed.
Magnetic anomaly detection and superconducting quantum interference device integration. Confirms metallic hull type, operational state, and orientation. Used in concert with GGEA for maritime target confirmation.
Atmospheric and oceanographic state modeling. Computes and distributes baseline environmental conditions to all other agents — wave state for SSEA, thermal background for TIREA, humidity profiles for LIDAR. The noise-floor establisher; every confidence computation is conditioned on ESEA's current state. Read-only from the perspective of other agents.
Active-sensor ranging and depth validation. Activated only on consensus events — to preserve stealth and power, LIDAREA does not run continuously. When SSEA and GGEA agree on a subsurface target, LIDAREA performs counter-stress ranging cycles to confirm presence and depth before ELA escalation. The validator of validators.
All six agents.Shared parameters.
Four primitives.One practical system.
The primitives that make the system runnable on edge hardware, verifiable by an ORSA, and defensible to a program manager.
Quantization
INT8 post-training quantization with selective FP16 retention on safety-critical attention heads. Delivers 70–85% memory footprint reduction against unquantized baselines with a measured accuracy delta below 2% on held-out target discrimination benchmarks. INT4 available for extreme-constraint hardware with 5–8% accuracy delta — approved for non-safety-critical pathways only.
Local RAG — Theater-Bounded
Every edge node carries a resident vector store indexed to relevant doctrine, rules of engagement, and threat libraries. The agent reasons against this local corpus only. No outbound retrieval query. No reachback for retrieval. Version-controlled corpus. Signed updates pushed during scheduled sync windows, never on demand. Air-gap tolerant — operates indefinitely with stale corpus, alerting on staleness but not failing.
Multi-Agent Consensus
No firing solution without cryptographic sign-off from both the Prime Agent and its adversarial stress-test team. The stress-test architecture mirrors a human engineering review board — commanding expert, deputy checker, calibration reviewer, and integrity auditor — operating at machine speed, without fatigue, continuously. False positives are not filtered post-hoc; they are prevented from forming.
Deterministic Output Layer
Temperature zero on all safety-critical inference paths. Fixed seeds. Compiled inference graphs with deterministic kernel selection. Two inferences on the same input produce bit-identical output. Post-run hash of output recorded for audit replay. This eliminates the failure modes common to consumer-grade generative AI: non-reproducible outputs, drift under identical inputs, and hallucinated classifications without traceable evidence.
Degraded, denied, intermittent,limited.
These are not edge cases. They are the design target. Every design decision was made under the assumption that the comms pipe will fail.
Runs onwhat you have.
No bespoke compute. No liquid cooling. No new infrastructure. The AAIS stack deploys on currently fielded hardware across every platform class.
Single-agent or two-agent configurations. ELA-lite. Warfighter-worn presentation gear.
Full six-agent stack. Full ELA. Reference platform for M-SHORAD, HIMARS, and vehicle-integrated applications.
Ship or aircraft integration. Full stack plus extended corpus. Redundant ELA for high-availability mission profiles.
Base-level installation — Fort Greely, missile defense sites, operations centers. Full stack plus multi-unit ELA serving multiple forward nodes.
On-orbit Expert Agent stack. Six+ satellite LEO constellation. Persistent multi-spectral homeland coverage. Consensus formed on-orbit, kilobyte packets to the shooter.
What this systemdoes not do.
Autonomous weapon release.
The system proposes engagements. It does not fire. Command authorization is the only write to the kill chain. This is an architectural invariant.
Generative output to the warfighter.
No freeform prose. No chatbot interface. The warfighter sees verified consensus, optimal pairing, and authorization status — in minimal form necessary to act.
Third-party cloud model inference.
OpenAI, Anthropic, and equivalent cloud models are not permitted on the critical path. Only AAIS-trained and quantized models run in the edge stack.
GPS / SATCOM dependency.
Neither GPS nor SATCOM are on the critical path for safety-critical operation. Positional reasoning derives from inertial, celestial, and sensor-inferred cues.
The system is built.The pilot is ninety days.
Full T&E in-region, at the unit of your choosing. Measurable operational delta by day 90.