Why We Built Autonomy
The supply chain planning industry is stuck. Here's our diagnosis, our guiding policy, and the coherent actions we're taking to fix it.
The Problem: Planning Hasn’t Changed in 20 Years
Most supply chain organizations operate the same way they did a decade ago. Planners open spreadsheets or legacy screens every Monday, review thousands of SKU-level exceptions, make judgment calls under time pressure, and hope the plan holds until next week.
When disruptions hit mid-cycle, the response is reactive: phone calls, expedited shipments, and costly overtime. The institutional knowledge that makes this work lives in the heads of a few senior planners, and when they leave, it leaves with them.
The Diagnosis
The planning function has three structural problems:
1. Periodic Cadence in a Continuous World
Supply chains don't wait for the Monday MPS run. A supplier delay on Tuesday, a demand spike on Wednesday, a quality hold on Thursday: each requires response, but the system only replans on schedule.
2. Human Bandwidth as the Bottleneck
A planner reviewing 847 exceptions per week can't distinguish signal from noise fast enough. By Thursday, she's reviewed 400. The other 447 roll into next week. Three of them were urgent. One caused a stockout.
3. Knowledge That Walks Out the Door
The rules, heuristics, and judgment calls that make planning work are undocumented and non-transferable. When a senior planner retires, 20 years of pattern recognition leaves with them.
Our Thesis
"Gartner designated Decision Intelligence 'transformational' in the 2025 AI Hype Cycle and published the inaugural Magic Quadrant for Decision Intelligence Platforms in January 2026. The shift is clear: from 'data-driven' to 'decision-centric.'"
Supply chain planning is ripe for what Jordi Visser calls the “agentic inversion”: the structural shift from human labor to machine execution. Not automation (the same tasks, faster) but inversion: agents own decisions by default, humans provide governance.
We believe the transition happens through a deliberate, measured progression:
Decision Support
Human in the loop. AI recommends, human decides.
Decision Augmentation
Human on the loop. AI decides within guardrails, human inspects.
Decision Automation
Human out of the loop. Overrides during augmentation are the training signal that enables automation.
The Guiding Policy: Automate-Inform-Inspect-Override
Autonomy doesn’t replace planners. It restructures what they spend their time on:
The planner becomes a manager of decisions, not a doer of tasks.
From 847 Exceptions to 14
What happens when an enterprise planner arrives Monday morning:
Auto-Resolved
High likelihood, agent decided
Abandoned
Low urgency + low likelihood
Informational
Handled, flagged for awareness
Inspect & Override
High urgency + low likelihood
She spends her morning on the 14 decisions where the agent needs help most: high urgency, low likelihood. She inspects each one, sees the agent’s reasoning, and overrides where her judgment is better. Every override teaches the system for next time.
The Self-Reinforcing Advantage
Every agent decision generates a decision-outcome pair. The agent observes the state (inventory levels, demand signals, capacity), takes an action (order, rebalance, defer), and then measures the outcome against a balanced scorecard. Actions that improved outcomes get reinforced. Actions that didn’t get dampened.
Unlike human planners who work 40 hours a week, agents work 168. Every hour of the night, every weekend, every bank holiday: agents are observing, deciding, and learning. They handle the repetitive and the mundane so planners can focus on the decisions that truly need human creativity.
Adoption Builds Trust Through Measurement
Our Approach: What We’re Building
We are not building another forecasting tool or dashboard. We are building a unified decision platform for supply chain, one shared world model driving six decision domains (portfolio, demand-shaping, supply, production, transport, warehouse), all surfaced through a single Decision Stream.
- Decisions as digital assets. Every recurring decision is a trackable asset with defined inputs, logic, ownership, and measured outcomes.
- Full decision lifecycle. Model, orchestrate, monitor, and govern decisions end-to-end.
- Research-grounded. Every architectural decision maps to peer-reviewed research in sequential decision-making, decision science, and conformal prediction.
- Enterprise-ready. AWS Supply Chain data model compliance, SAP integration, RBAC, full audit trail.
Autonomous Agents
Continuous Operation
Decision Latency
AWS SC Entities
See Autonomy in action
Walk through how Autonomy models, executes, monitors, and governs supply chain decisions with autonomous AI agents.