What happens when an agentic AI system is responsible for resolving trade breaks across 103 million daily transactions — with zero tolerance for error and a T+1 regulatory clock ticking?
This session introduces ReconGraph, an agentic graph-reasoning framework purpose-built for post-trade reconciliation in enterprise securities operations. Unlike traditional rule-based exception management, ReconGraph deploys autonomous AI agents that traverse a live knowledge graph of trades, counterparties, custodians, and settlement states — discovering break patterns, classifying root causes, and recommending resolution pathways with full audit traceability.
Attendees will walk through the architecture of a production-grade agentic system operating under FINRA and SEC compliance constraints, including how agent decision boundaries are enforced, how human-in-the-loop escalation is triggered, and how every agent action is logged to satisfy regulatory audit expectations. This is not a proof-of-concept — it is a practitioner's blueprint from the front lines of T+1 settlement modernization.
Key takeaways: graph-augmented agent design patterns for financial workflows, compliance-aware agent boundaries, and lessons from deploying autonomous AI in zero-downtime fault-tolerant infrastructure.