Research-backed architecture
Technology
Built on peer-reviewed research, enterprise data standards, and a decade of supply chain optimization practice.
The engine behind the Decision Stream: one shared world model, four tiers of coordination, and the AIIO operating model.
Four Tiers of Decision Coordination
Context, guardrails, and targets flow down as policy and directives. Feedback and outcomes flow back up. Each tier operates at its natural time horizon.
Strategic / Network
Strategic · Weekly / Monthly
Network-wide policy optimization. Sets guardrails, KPI targets, and policy parameters that shape all downstream behavior. Design, IBP, and S&OP decisions.
Tactical / Network
Tactical · Daily
Translates strategic directives into site-specific context. Generates demand forecasts, priority allocations, and cross-site trade-offs. Cross-functional negotiations resolve at machine speed.
Operational / Site Coordination (per site)
Operational · Hourly
Each site has its own coordination layer. Learns causal relationships between the site's execution agents, predicts cascade effects (a production spike will create quality load hours later), and pre-adjusts urgency before cascades unfold.
Execution / Site & Role (per site)
Execution · <10ms per decision
Each site runs its own hive of 11 specialized agents making narrow decisions at machine speed: ATP, purchase orders, rebalancing, manufacturing, quality, maintenance, and more. Each agent decides within guardrails set by the tiers above.
Remember: Velocity creates value.
Platform Architecture
Four tiers of agent-driven decisions. Context Engine routes external signals into each tier by horizon. Experiential Knowledge shapes agent priors forward and captures overrides for retraining back. The Digital Twin trains agents — it is not a planning surface.
Decision Intelligence Architecture
Autonomy is built as a Decision Intelligence Platform following Gartner's DIP framework (inaugural MQ, January 2026). The architecture implements all four DI lifecycle capabilities natively for supply chain.
Architecture informed by decision science research - turning information into better actions, mapping decision levers through intermediaries to outcomes. Concept adapted from FlexRule's DI Architecture and Wikipedia: Decision Intelligence.
Platform Architecture (Detailed)
Four decision tiers cascade context, targets, and guardrails downward while feeding escalation and feedback upward. From strategic network design through tactical planning, operational coordination, and sub-second execution, every layer is connected by a unified data model, experiential knowledge layer, and integration fabric.
Sequential Decision Framework
Autonomy's four-tier agent stack instantiates Warren Powell's Sequential Decision Analytics (SDAM) framework. Each tier uses the policy class that fits its horizon and scope. The Digital Twin is the training environment that pre-trains these agents — it does not generate plans.
Execution Agent Architecture
Each agent uses a compact, purpose-built architecture that achieves near-optimal performance while running at <10ms per decision.
Small, focused models that learn generalizable rules rather than memorizing patterns. This keeps agents fast, explainable, and reliable in production.
Conformal Prediction
Confidence Guarantees
Every AI decision carries a distribution-free confidence guarantee via conformal prediction. Unlike heuristic confidence scores that require distributional assumptions, conformal prediction provides mathematically calibrated coverage: the true outcome falls within the prediction set with guaranteed probability, regardless of the underlying data distribution.
This guarantee holds even when the model is wrong - misspecification widens the prediction set but never breaks the coverage guarantee.
Concept adapted from Awesome Conformal Prediction and BBVA AI Factory.
Coverage guarantee holds regardless of the underlying data distribution - no Gaussian assumptions needed.
Runs continuously from historical decision-outcome pairs. Adapts to seasonal demand, supplier disruptions, market shifts.
When prediction set size grows, decisions automatically route to higher reasoning tiers for more thorough analysis.
Concept based on Interpretable ML - Counterfactual Explanations (CC BY-NC-SA 4.0).
Causal AI
Decision Attribution
Knowing an outcome is good is not enough - you need to know whether the decision caused the good outcome. Autonomy uses counterfactual reasoning to evaluate every agent decision: what actually happened vs. what would have happened if the agent had decided differently.
This causal attribution drives everything downstream. Agent training weights decisions by causal impact, not outcome correlation. Override effectiveness is measured by comparing the human's outcome against the agent's counterfactual.
Without causal reasoning, an autonomous system reinforces lucky decisions and punishes skillful decisions that encountered bad luck. Causal AI ensures the system gets genuinely smarter with every decision cycle.
Agentic Authorization Protocol
When an agent needs to take an action outside its authority boundary, the AAP provides machine-speed negotiation across affected agents.
The Hive's cross-authority layer — peer coordination between sites, built on the same A2A foundation as the per-site Hive.
Multi-agent coordination pattern inspired by Google A2A Protocol and NVIDIA Multi-Agent Warehouse AI.
AWS Supply Chain Data Model
Escalation Arbiter
Inspired by dual-process cognitive theory, monitors agent decision quality over time.
SCOR Metrics Hierarchy
Industry-standard SCOR (Supply Chain Operations Reference) metrics framework across three levels that cascade from strategic assessment to operational correction.
Based on the ASCM SCOR Digital Standard. Metrics hierarchy follows the SCOR v12.0 Quick Reference.
Dive deeper
Talk to our team about the technical architecture behind Autonomy.