Welcome to FourKites Loft
AI Orchestration Platform That Works Across Any Enterprise System with Intelligence from Outside Your Four Walls
ENTER THE LOFT
AI Orchestration Platform That Works Across Any Enterprise System with Intelligence from Outside Your Four Walls
ENTER THE LOFT
When AI agents learn only from their own past decisions and internal data, they suffer from “cognitive gravity”—drifting toward repetitive, safe, but ultimately suboptimal choices. To keep supply chain agents from collapsing into a feedback loop of “visual elevator music,” we must anchor them to external, real-world signals. At FourKites, that anchor is our real-time network event stream.
A recent paper titled Autonomous language-image generation loops converge to generic visual motifs has been circulating among engineering leaders. Researchers linked image generators and vision models into closed feedback loops. One model created images. Another described them. The first re-created them.
They started with 700 diverse prompts. They expected the models to explore new creative frontiers.
Instead, the models collapsed.
Diversity vanished, and the systems drifted toward a tiny set of repetitive visual tropes. They generated “stormy lighthouses” and “gothic cathedrals” over and over again.
The researchers called this “visual elevator music.”
Paul Kedrosky described it as “cognitive gravity,” which sets in when a system feeds on its own outputs. It optimizes for the mathematical average and stops exploring.
Other researchers are finding similar collapse patterns across domains. If you’re building autonomous systems, that stormy lighthouse should worry you.
At FourKites, we’re shifting from chatbots that write emails to agents that run supply chains. If we build these agents on closed loops, training them only on internal data and their own past decisions, they’ll drift.
Imagine an agent that learns mainly from precedent. It might query your internal history to decide how to handle a shipment that is four hours late. It retrieves three years of similar cases and sees that your team rarely expedited. It treats this pattern as guidance. The next time a shipment is late, the agent waves it through. It logs that decision for future retrieval.
The agent lacks context. It doesn’t know why the humans ignored those past delays. It just knows they did. Over time, the system drifts toward decisions that are operationally defensible rather than optimal — safe choices that don’t trigger exceptions or require escalation.
We faced this challenge when architecting Loft, our AI orchestration platform, and Sophie, an AI developer agent that turns plain-English operational requirements into production-ready automations. Rather than building an agent that learns strictly from internal history, we designed a system anchored to goals and real-world outcomes.
Agents need an external signal, something that disrupts their internal assumptions. For us, that’s our network’s real-time event stream: millions of daily physical events across hundreds of thousands of trading partners. Because the agent is watching what’s actually happening on the ground, internal precedent doesn’t get the final word. The physical world corrects the drift — exactly what the researchers found missing in their image loops.
For anyone building autonomous systems, the research raises questions worth sitting with. Where does your agent get its corrective signal? What happens if it only learns from itself? How do you know when drift has already set in?
If your AI agents are only looking at your past, they can’t help you build your future. See how the FourKites Loft uses real-time event streams to break the feedback loop and drive optimal outcomes across your entire supply chain.
